Sample records for n-adaptive fuzzy models

  1. Adaptive time-variant models for fuzzy-time-series forecasting.

    PubMed

    Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

    2010-12-01

    A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

  2. Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.

    PubMed

    Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun

    2016-10-01

    This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.

  3. Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

    PubMed

    Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

    2011-01-01

    In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

  4. Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

    NASA Technical Reports Server (NTRS)

    Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

    2003-01-01

    Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

  5. Method study on fuzzy-PID adaptive control of electric-hydraulic hitch system

    NASA Astrophysics Data System (ADS)

    Li, Mingsheng; Wang, Liubu; Liu, Jian; Ye, Jin

    2017-03-01

    In this paper, fuzzy-PID adaptive control method is applied to the control of tractor electric-hydraulic hitch system. According to the characteristics of the system, a fuzzy-PID adaptive controller is designed and the electric-hydraulic hitch system model is established. Traction control and position control performance simulation are carried out with the common PID control method. A field test rig was set up to test the electric-hydraulic hitch system. The test results showed that, after the fuzzy-PID adaptive control is adopted, when the tillage depth steps from 0.1m to 0.3m, the system transition process time is 4s, without overshoot, and when the tractive force steps from 3000N to 7000N, the system transition process time is 5s, the system overshoot is 25%.

  6. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    PubMed Central

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  7. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    NASA Astrophysics Data System (ADS)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  8. Decomposed fuzzy systems and their application in direct adaptive fuzzy control.

    PubMed

    Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

    2014-10-01

    In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

  9. Genetic algorithms in adaptive fuzzy control

    NASA Technical Reports Server (NTRS)

    Karr, C. Lucas; Harper, Tony R.

    1992-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

  10. Adaptive inferential sensors based on evolving fuzzy models.

    PubMed

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the

  11. Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines

    NASA Astrophysics Data System (ADS)

    Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

    2014-10-01

    In this paper nonlinear embedded control for turbocharged Diesel engines is developed with the use of Differential flatness theory and adaptive fuzzy control. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances an adaptive fuzzy control scheme is implemanted making use of the transformed dynamical system of the diesel engine that is obtained through the application of differential flatness theory. Since only the system's output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for neuro-fuzzy approximators, which are part of the controller, so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed observer-based adaptive fuzzy control scheme results in H∞ tracking performance.

  12. Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

    NASA Technical Reports Server (NTRS)

    Esogbue, Augustine O.

    1998-01-01

    The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of

  13. Road sign recognition with fuzzy adaptive pre-processing models.

    PubMed

    Lin, Chien-Chuan; Wang, Ming-Shi

    2012-01-01

    A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

  14. Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

    PubMed Central

    Lin, Chien-Chuan; Wang, Ming-Shi

    2012-01-01

    A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

  15. HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

    PubMed

    Kim, J; Kasabov, N

    1999-11-01

    This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

  16. Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

    NASA Astrophysics Data System (ADS)

    Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

    2007-12-01

    The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

  17. Abrasive slurry jet cutting model based on fuzzy relations

    NASA Astrophysics Data System (ADS)

    Qiang, C. H.; Guo, C. W.

    2017-12-01

    The cutting process of pre-mixed abrasive slurry or suspension jet (ASJ) is a complex process affected by many factors, and there is a highly nonlinear relationship between the cutting parameters and cutting quality. In this paper, guided by fuzzy theory, the fuzzy cutting model of ASJ was developed. In the modeling of surface roughness, the upper surface roughness prediction model and the lower surface roughness prediction model were established respectively. The adaptive fuzzy inference system combines the learning mechanism of neural networks and the linguistic reasoning ability of the fuzzy system, membership functions, and fuzzy rules are obtained by adaptive adjustment. Therefore, the modeling process is fast and effective. In this paper, the ANFIS module of MATLAB fuzzy logic toolbox was used to establish the fuzzy cutting model of ASJ, which is found to be quite instrumental to ASJ cutting applications.

  18. High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

    NASA Astrophysics Data System (ADS)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

    2008-02-01

    Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

  19. Adaptive fuzzy system for 3-D vision

    NASA Technical Reports Server (NTRS)

    Mitra, Sunanda

    1993-01-01

    An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

  20. Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.

    PubMed

    Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

    2013-05-01

    In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

  1. Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

    PubMed

    Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

    2017-09-01

    This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

  2. Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

    PubMed

    Wang, Wei; Tong, Shaocheng

    2018-02-01

    This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.

  3. Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

    PubMed

    Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

    2011-04-01

    This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

  4. An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

    PubMed Central

    Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

    2016-01-01

    This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

  5. Design of sewage treatment system by applying fuzzy adaptive PID controller

    NASA Astrophysics Data System (ADS)

    Jin, Liang-Ping; Li, Hong-Chan

    2013-03-01

    In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.

  6. Fuzzy Edge Connectivity of Graphical Fuzzy State Space Model in Multi-connected System

    NASA Astrophysics Data System (ADS)

    Harish, Noor Ainy; Ismail, Razidah; Ahmad, Tahir

    2010-11-01

    Structured networks of interacting components illustrate complex structure in a direct or intuitive way. Graph theory provides a mathematical modeling for studying interconnection among elements in natural and man-made systems. On the other hand, directed graph is useful to define and interpret the interconnection structure underlying the dynamics of the interacting subsystem. Fuzzy theory provides important tools in dealing various aspects of complexity, imprecision and fuzziness of the network structure of a multi-connected system. Initial development for systems of Fuzzy State Space Model (FSSM) and a fuzzy algorithm approach were introduced with the purpose of solving the inverse problems in multivariable system. In this paper, fuzzy algorithm is adapted in order to determine the fuzzy edge connectivity between subsystems, in particular interconnected system of Graphical Representation of FSSM. This new approach will simplify the schematic diagram of interconnection of subsystems in a multi-connected system.

  7. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  8. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    PubMed

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  9. Introduction to Fuzzy Set Theory

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  10. Nonlinear adaptive control based on fuzzy sliding mode technique and fuzzy-based compensator.

    PubMed

    Nguyen, Sy Dzung; Vo, Hoang Duy; Seo, Tae-Il

    2017-09-01

    It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    PubMed Central

    Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

    2014-01-01

    A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

  12. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

    PubMed Central

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-01-01

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

  13. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

    PubMed

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-12-16

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

  14. Adaptive fuzzy logic controller with direct action type structures for InnoSAT attitude control system

    NASA Astrophysics Data System (ADS)

    Bakri, F. A.; Mashor, M. Y.; Sharun, S. M.; Bibi Sarpinah, S. N.; Abu Bakar, Z.

    2016-10-01

    This study proposes an adaptive fuzzy controller for attitude control system (ACS) of Innovative Satellite (InnoSAT) based on direct action type structure. In order to study new methods used in satellite attitude control, this paper presents three structures of controllers: Fuzzy PI, Fuzzy PD and conventional Fuzzy PID. The objective of this work is to compare the time response and tracking performance among the three different structures of controllers. The parameters of controller were tuned on-line by adjustment mechanism, which was an approach similar to a PID error that could minimize errors between actual and model reference output. This paper also presents a Model References Adaptive Control (MRAC) as a control scheme to control time varying systems where the performance specifications were given in terms of the reference model. All the controllers were tested using InnoSAT system under some operating conditions such as disturbance, varying gain, measurement noise and time delay. In conclusion, among all considered DA-type structures, AFPID controller was observed as the best structure since it outperformed other controllers in most conditions.

  15. eFSM--a novel online neural-fuzzy semantic memory model.

    PubMed

    Tung, Whye Loon; Quek, Chai

    2010-01-01

    Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This

  16. Fuzzy Adaptive Decentralized Optimal Control for Strict Feedback Nonlinear Large-Scale Systems.

    PubMed

    Sun, Kangkang; Sui, Shuai; Tong, Shaocheng

    2018-04-01

    This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.

  17. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

    PubMed

    Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

    2016-07-26

    This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

  18. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    PubMed Central

    Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

    2016-01-01

    This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336

  19. Adaptive fuzzy sliding control of single-phase PV grid-connected inverter

    PubMed Central

    Zhu, Yunkai

    2017-01-01

    In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance. PMID:28797060

  20. Adaptive fuzzy sliding control of single-phase PV grid-connected inverter.

    PubMed

    Fei, Juntao; Zhu, Yunkai

    2017-01-01

    In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.

  1. An experimental methodology for a fuzzy set preference model

    NASA Technical Reports Server (NTRS)

    Turksen, I. B.; Willson, Ian A.

    1992-01-01

    A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate

  2. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  3. Intuitionistic fuzzy n-fold KU-ideal of KU-algebra

    NASA Astrophysics Data System (ADS)

    Mostafa, Samy M.; Kareem, Fatema F.

    2018-05-01

    In this paper, we apply the notion of intuitionistic fuzzy n-fold KU-ideal of KU-algebra. Some types of ideals such as intuitionistic fuzzy KU-ideal, intuitionistic fuzzy closed ideal and intuitionistic fuzzy n-fold KU-ideal are studied. Also, the relations between intuitionistic fuzzy n-fold KU-ideal and intuitionistic fuzzy KU-ideal are discussed. Furthermore, a few results of intuitionistic fuzzy n-fold KU-ideals of a KU-algebra under homomorphism are discussed.

  4. Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems.

    PubMed

    Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi

    2015-05-01

    In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Adaptive process control using fuzzy logic and genetic algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  6. A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

    PubMed Central

    Kwong, C. K.; Fung, K. Y.; Jiang, Huimin; Chan, K. Y.

    2013-01-01

    Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort. PMID:24385884

  7. A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

    PubMed

    Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

    2013-01-01

    Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

  8. Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

    NASA Astrophysics Data System (ADS)

    Chai, Runqi; Savvaris, Al; Tsourdos, Antonios

    2016-06-01

    In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.

  9. A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

    NASA Astrophysics Data System (ADS)

    Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

    2013-07-01

    The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

  10. A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

    NASA Astrophysics Data System (ADS)

    Porwal, A.; Carranza, J.; Hale, M.

    2004-12-01

    A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

  11. Adaptive neuro fuzzy system for modelling and prediction of distance pantograph catenary in railway transportation

    NASA Astrophysics Data System (ADS)

    Panoiu, M.; Panoiu, C.; Lihaciu, I. L.

    2018-01-01

    This research presents an adaptive neuro-fuzzy system which is used in the prediction of the distance between the pantograph and contact line of the electrical locomotives used in railway transportation. In railway transportation any incident that occurs in the electrical system can have major negative effects: traffic interrupts, equipment destroying. Therefore, a prediction as good as possible of such situations is very useful. In the paper was analyzing the possibility of modeling and prediction the variation of the distance between the pantograph and the contact line using intelligent techniques

  12. Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

    PubMed

    Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

    2016-03-01

    In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift

    NASA Astrophysics Data System (ADS)

    Arfan Jaffar, M.

    2017-01-01

    In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.

  14. Fuzzy support vector machines for adaptive Morse code recognition.

    PubMed

    Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

    2006-11-01

    Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

  15. Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone.

    PubMed

    Tong, Shaocheng; Sui, Shuai; Li, Yongming

    2015-12-01

    In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

  16. An Adaptive Supervisory Sliding Fuzzy Cerebellar Model Articulation Controller for Sensorless Vector-Controlled Induction Motor Drive Systems

    PubMed Central

    Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

    2015-01-01

    This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes. PMID:25815450

  17. An adaptive supervisory sliding fuzzy cerebellar model articulation controller for sensorless vector-controlled induction motor drive systems.

    PubMed

    Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

    2015-03-25

    This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes--the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC--were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.

  18. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  19. The Study and Design of Adaptive Learning System Based on Fuzzy Set Theory

    NASA Astrophysics Data System (ADS)

    Jia, Bing; Zhong, Shaochun; Zheng, Tianyang; Liu, Zhiyong

    Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

  20. Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics.

    PubMed

    Li, Yongming; Sui, Shuai; Tong, Shaocheng

    2017-02-01

    This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

  1. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

    PubMed Central

    Hosseini, Monireh Sheikh; Zekri, Maryam

    2012-01-01

    Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

  2. Modal-space reference-model-tracking fuzzy control of earthquake excited structures

    NASA Astrophysics Data System (ADS)

    Park, Kwan-Soon; Ok, Seung-Yong

    2015-01-01

    This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.

  3. Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

    PubMed

    Tong, Shaocheng; Li, Yongming

    2017-02-01

    This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

  4. Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

    NASA Astrophysics Data System (ADS)

    Tsaur, Ruey-Chyn

    2015-02-01

    In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

  5. A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

    NASA Astrophysics Data System (ADS)

    Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

    This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

  6. Robust adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with input constraints.

    PubMed

    Wang, Huanqing; Chen, Bing; Liu, Xiaoping; Liu, Kefu; Lin, Chong

    2013-12-01

    This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

  7. System identification of smart structures using a wavelet neuro-fuzzy model

    NASA Astrophysics Data System (ADS)

    Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

    2012-11-01

    This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

  8. Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Wang, Rongrong; Qi, Liang; Xie, Xiaofeng; Ding, Qingqing; Li, Chunwen; Ma, ChenChi M.

    The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system.

  9. A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

    NASA Astrophysics Data System (ADS)

    Dalkilic, Turkan Erbay; Apaydin, Aysen

    2009-11-01

    In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

  10. Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

    PubMed

    Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

    2017-04-01

    We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its

  11. Incremental Adaptive Fuzzy Control for Sensorless Stroke Control of A Halbach-type Linear Oscillatory Motor

    NASA Astrophysics Data System (ADS)

    Lei, Meizhen; Wang, Liqiang

    2018-01-01

    The halbach-type linear oscillatory motor (HT-LOM) is multi-variable, highly coupled, nonlinear and uncertain, and difficult to get a satisfied result by conventional PID control. An incremental adaptive fuzzy controller (IAFC) for stroke tracking was presented, which combined the merits of PID control, the fuzzy inference mechanism and the adaptive algorithm. The integral-operation is added to the conventional fuzzy control algorithm. The fuzzy scale factor can be online tuned according to the load force and stroke command. The simulation results indicate that the proposed control scheme can achieve satisfied stroke tracking performance and is robust with respect to parameter variations and external disturbance.

  12. Adaptive Fuzzy Output-Constrained Fault-Tolerant Control of Nonlinear Stochastic Large-Scale Systems With Actuator Faults.

    PubMed

    Li, Yongming; Ma, Zhiyao; Tong, Shaocheng

    2017-09-01

    The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

  13. Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

    NASA Astrophysics Data System (ADS)

    Chang, N.

    2006-12-01

    The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals

  14. Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.

    PubMed

    Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

    2013-06-01

    The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

  15. Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

    NASA Astrophysics Data System (ADS)

    Ibrahim, Wael Refaat Anis

    The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an

  16. Fuzzy intelligent quality monitoring model for X-ray image processing.

    PubMed

    Khalatbari, Azadeh; Jenab, Kouroush

    2009-01-01

    Today's imaging diagnosis needs to adapt modern techniques of quality engineering to maintain and improve its accuracy and reliability in health care system. One of the main factors that influences diagnostic accuracy of plain film X-ray on detecting pathology is the level of film exposure. If the level of film exposure is not adequate, a normal body structure may be interpretated as pathology and vice versa. This not only influences the patient management but also has an impact on health care cost and patient's quality of life. Therefore, providing an accurate and high quality image is the first step toward an excellent patient management in any health care system. In this paper, we study these techniques and also present a fuzzy intelligent quality monitoring model, which can be used to keep variables from degrading the image quality. The variables derived from chemical activity, cleaning procedures, maintenance, and monitoring may not be sensed, measured, or calculated precisely due to uncertain situations. Therefore, the gamma-level fuzzy Bayesian model for quality monitoring of an image processing is proposed. In order to apply the Bayesian concept, the fuzzy quality characteristics are assumed as fuzzy random variables. Using the fuzzy quality characteristics, the newly developed model calculates the degradation risk for image processing. A numerical example is also presented to demonstrate the application of the model.

  17. Fuzzy object modeling

    NASA Astrophysics Data System (ADS)

    Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.

    2011-03-01

    To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.

  18. Adaptive fuzzy controller for thermal comfort inside the air-conditioned automobile chamber

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Tong, L.; Yu, B.; Chen, Z.

    1999-07-01

    In order to meet the passengers' demand for thermal comfort, the adaptive fuzzy logic control design methodology is applied for the automobile airconditioner system. In accordance with the theory of air flow and heat transfer, the air temperature field inside the airconditioned automobile chamber is simulated by a set of simplified half-empirical formula. Then, instead of PMV (Predicted Mean Vote) criterion, RIV (Real Individual Vote) criterion is adopted as the base of the control for passengers' thermal comfort. The proposed controller is applied to the air temperature regulation at the individual passenger position. The control procedure is based on partitioningmore » the state space of the system into cell-groups and fuzzily quantificating the state space into these cells. When the system model has some parameter perturbation, the controller can also adjust its control parameters to compensate for the perturbation and maintain the good performance. The learning procedure shows its ideal effect in both computer simulation and experiments. The final results demonstrate the ideal performance of this adaptive fuzzy controller.« less

  19. Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot

    PubMed Central

    Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar

    2016-01-01

    A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062

  20. Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay.

    PubMed

    Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan

    2015-11-01

    In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Fuzzy logic of Aristotelian forms

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Perlovsky, L.I.

    1996-12-31

    Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties.more » In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.« less

  2. Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

    NASA Astrophysics Data System (ADS)

    Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

    A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

  3. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    PubMed Central

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  4. Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

    NASA Astrophysics Data System (ADS)

    Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

    2018-04-01

    In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

  5. Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

    PubMed

    Maharlou, Hamidreza; Niakan Kalhori, Sharareh R; Shahbazi, Shahrbanoo; Ravangard, Ramin

    2018-04-01

    Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

  6. Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the Estimation of Depth of Anaesthesia.

    PubMed

    Benzy, V K; Jasmin, E A; Koshy, Rachel Cherian; Amal, Frank; Indiradevi, K P

    2018-01-01

    The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available on electroencephalogram signals during anaesthesia are drawn by extracting relative wave energy features from the anaesthetic electroencephalogram signals. Discrete wavelet transform is used to decomposes the electroencephalogram signals into four levels and then relative wave energy is computed from approximate and detail coefficients of sub-band signals. Relative wave energy is extracted to find out the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases awake, induction, maintenance and recovery. The Kruskal-Wallis statistical test is applied on the relative wave energy features to check the discriminating capability of relative wave energy features as awake, light anaesthesia, moderate anaesthesia and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing a Adaptive neuro-fuzzy inference system based fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor Bispectral index.

  7. Adaptive fuzzy leader clustering of complex data sets in pattern recognition

    NASA Technical Reports Server (NTRS)

    Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

    1992-01-01

    A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

  8. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

    PubMed

    Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Moghaddam, Asghar Asghari

    2017-03-01

    Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

  9. Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications

    NASA Astrophysics Data System (ADS)

    Ribeiro, Moisés V.

    2004-12-01

    This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.

  10. Automation of a portable extracorporeal circulatory support system with adaptive fuzzy controllers.

    PubMed

    Mendoza García, A; Krane, M; Baumgartner, B; Sprunk, N; Schreiber, U; Eichhorn, S; Lange, R; Knoll, A

    2014-08-01

    The presented work relates to the procedure followed for the automation of a portable extracorporeal circulatory support system. Such a device may help increase the chances of survival after suffering from cardiogenic shock outside the hospital, additionally a controller can provide of optimal organ perfusion, while reducing the workload of the operator. Animal experiments were carried out for the acquisition of haemodynamic behaviour of the body under extracorporeal circulation. A mathematical model was constructed based on the experimental data, including a cardiovascular model, gas exchange and the administration of medication. As the base of the controller fuzzy logic was used allowing the easy integration of knowledge from trained perfusionists, an adaptive mechanism was included to adapt to the patient's individual response. Initial simulations show the effectiveness of the controller and the improvements of perfusion after adaptation. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

  11. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    NASA Astrophysics Data System (ADS)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  12. Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.

    PubMed

    Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu

    2015-12-01

    This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.

  13. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  14. Markowitz portfolio optimization model employing fuzzy measure

    NASA Astrophysics Data System (ADS)

    Ramli, Suhailywati; Jaaman, Saiful Hafizah

    2017-04-01

    Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.

  15. Component Models for Fuzzy Data

    ERIC Educational Resources Information Center

    Coppi, Renato; Giordani, Paolo; D'Urso, Pierpaolo

    2006-01-01

    The fuzzy perspective in statistical analysis is first illustrated with reference to the "Informational Paradigm" allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to "J" LR fuzzy variables as observed on "I"…

  16. Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

    NASA Astrophysics Data System (ADS)

    Nourmohammadi, Hossein; Keighobadi, Jafar

    2018-01-01

    Due to weak stand-alone accuracy as well as poor run-to-run stability of micro-electro mechanical system (MEMS)-based inertial sensors, special approaches are required to integrate low-cost strap-down inertial navigation system (SINS) with global positioning system (GPS), particularly in long-term applications. This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme. The main concept behind the proposed adaptive integration is the good performance of attitude-heading reference system (AHRS) in low-accelerated motions and its degradation in maneuvered or accelerated motions. Depending on vehicle maneuvers, gravity-based attitude angles can be intelligently utilized to improve orientation estimation in the SINS. Knowledge-based fuzzy inference system is developed for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions. Inertial measurements are the main input data of the fuzzy system to determine the maneuvering level during the vehicle motions. Accordingly, appropriate weighting coefficients are produced to combine the SINS/GPS and the AHRS, efficiently. The assessment of the proposed integrated navigation system is conducted via real data in airborne tests.

  17. Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

    NASA Astrophysics Data System (ADS)

    Huang, Shiuh-Jer; Chen, Hung-Yi

    In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

  18. Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran

    NASA Astrophysics Data System (ADS)

    Shiroodi, Sadjad Kazem; Ghafoori, Mohammad; Ansari, Hamid Reza; Lashkaripour, Golamreza; Ghanadian, Mostafa

    2017-02-01

    The main purpose of this study is to introduce the geological controlling factors in improving an intelligence-based model to estimate shear wave velocity from seismic attributes. The proposed method includes three main steps in the framework of geological events in a complex sedimentary succession located in the Persian Gulf. First, the best attributes were selected from extracted seismic data. Second, these attributes were transformed into shear wave velocity using fuzzy inference systems (FIS) such as Sugeno's fuzzy inference (SFIS), adaptive neuro-fuzzy inference (ANFIS) and optimized fuzzy inference (OFIS). Finally, a committee fuzzy machine (CFM) based on bat-inspired algorithm (BA) optimization was applied to combine previous predictions into an enhanced solution. In order to show the geological effect on improving the prediction, the main classes of predominate lithofacies in the reservoir of interest including shale, sand, and carbonate were selected and then the proposed algorithm was performed with and without lithofacies constraint. The results showed a good agreement between real and predicted shear wave velocity in the lithofacies-based model compared to the model without lithofacies especially in sand and carbonate.

  19. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement.

    PubMed

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-12-03

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

  20. Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

    PubMed Central

    Chiang, Shu-Yin; Kan, Yao-Chiang; Chen, Yun-Shan; Tu, Ying-Ching; Lin, Hsueh-Chun

    2016-01-01

    Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC. PMID:27918482

  1. On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach.

    PubMed

    Baigzadehnoe, Barmak; Rahmani, Zahra; Khosravi, Alireza; Rezaie, Behrooz

    2017-09-01

    In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

    PubMed

    Li, Yongming; Tong, Shaocheng

    2017-12-01

    In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

  3. Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

    NASA Technical Reports Server (NTRS)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

    Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

  4. Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

    NASA Astrophysics Data System (ADS)

    Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat

    2014-07-01

    The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  5. Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter

    NASA Astrophysics Data System (ADS)

    Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.

    2008-06-01

    This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dqγ). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.

  6. ASICs Approach for the Implementation of a Symmetric Triangular Fuzzy Coprocessor and Its Application to Adaptive Filtering

    NASA Technical Reports Server (NTRS)

    Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan

    1997-01-01

    This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.

  7. Adaptive Jacobian Fuzzy Attitude Control for Flexible Spacecraft Combined Attitude and Sun Tracking System

    NASA Astrophysics Data System (ADS)

    Chak, Yew-Chung; Varatharajoo, Renuganth

    2016-07-01

    Many spacecraft attitude control systems today use reaction wheels to deliver precise torques to achieve three-axis attitude stabilization. However, irrecoverable mechanical failure of reaction wheels could potentially lead to mission interruption or total loss. The electrically-powered Solar Array Drive Assemblies (SADA) are usually installed in the pitch axis which rotate the solar arrays to track the Sun, can produce torques to compensate for the pitch-axis wheel failure. In addition, the attitude control of a flexible spacecraft poses a difficult problem. These difficulties include the strong nonlinear coupled dynamics between the rigid hub and flexible solar arrays, and the imprecisely known system parameters, such as inertia matrix, damping ratios, and flexible mode frequencies. In order to overcome these drawbacks, the adaptive Jacobian tracking fuzzy control is proposed for the combined attitude and sun-tracking control problem of a flexible spacecraft during attitude maneuvers in this work. For the adaptation of kinematic and dynamic uncertainties, the proposed scheme uses an adaptive sliding vector based on estimated attitude velocity via approximate Jacobian matrix. The unknown nonlinearities are approximated by deriving the fuzzy models with a set of linguistic If-Then rules using the idea of sector nonlinearity and local approximation in fuzzy partition spaces. The uncertain parameters of the estimated nonlinearities and the Jacobian matrix are being adjusted online by an adaptive law to realize feedback control. The attitude of the spacecraft can be directly controlled with the Jacobian feedback control when the attitude pointing trajectory is designed with respect to the spacecraft coordinate frame itself. A significant feature of this work is that the proposed adaptive Jacobian tracking scheme will result in not only the convergence of angular position and angular velocity tracking errors, but also the convergence of estimated angular velocity to

  8. Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

    PubMed

    Lin, Chuan-Kai

    2005-04-01

    A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

  9. Design of a self-adaptive fuzzy PID controller for piezoelectric ceramics micro-displacement system

    NASA Astrophysics Data System (ADS)

    Zhang, Shuang; Zhong, Yuning; Xu, Zhongbao

    2008-12-01

    In order to improve control precision of the piezoelectric ceramics (PZT) micro-displacement system, a self-adaptive fuzzy Proportional Integration Differential (PID) controller is designed based on the traditional digital PID controller combining with fuzzy control. The arithmetic gives a fuzzy control rule table with the fuzzy control rule and fuzzy reasoning, through this table, the PID parameters can be adjusted online in real time control. Furthermore, the automatic selective control is achieved according to the change of the error. The controller combines the good dynamic capability of the fuzzy control and the high stable precision of the PID control, adopts the method of using fuzzy control and PID control in different segments of time. In the initial and middle stage of the transition process of system, that is, when the error is larger than the value, fuzzy control is used to adjust control variable. It makes full use of the fast response of the fuzzy control. And when the error is smaller than the value, the system is about to be in the steady state, PID control is adopted to eliminate static error. The problems of PZT existing in the field of precise positioning are overcome. The results of the experiments prove that the project is correct and practicable.

  10. Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

    PubMed

    Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

    2017-07-01

    In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. A novel fuzzy-logic control strategy minimizing N2O emissions.

    PubMed

    Boiocchi, Riccardo; Gernaey, Krist V; Sin, Gürkan

    2017-10-15

    A novel control strategy for achieving low N 2 O emissions and low effluent NH 4 + concentration is here proposed. The control strategy uses the measurements of ammonium and nitrate concentrations in inlet and outlet of the aerobic zone of a wastewater treatment plant to calculate a ratio indicating the balance among the microbial groups. More specifically, the ratio will indicate if there is a complete nitrification. In case nitrification is not complete, the controller will adjust the aeration level of the plant in order to inhibit the production of N 2 O from AOB and HB denitrification. The controller was implemented using the fuzzy logic approach. It was comprehensively tested for different model structures and different sets of model parameters with regards to its ability of mitigating N 2 O emissions for future applications in real wastewater treatment plants. It is concluded that the control strategy is useful for those plants having AOB denitrification as the main N 2 O producing process. However, in treatment plants having incomplete NH 2 OH oxidation as the main N 2 O producing pathway, a cascade controller configuration adapting the oxygen supply to respect only the effluent ammonium concentration limits was found to be more effective to ensure low N 2 O emissions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Comparison of Fuzzy-Based Models in Landslide Hazard Mapping

    NASA Astrophysics Data System (ADS)

    Mijani, N.; Neysani Samani, N.

    2017-09-01

    Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

  13. Adding dynamic rules to self-organizing fuzzy systems

    NASA Technical Reports Server (NTRS)

    Buhusi, Catalin V.

    1992-01-01

    This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

  14. Mathematical models of the simplest fuzzy PI/PD controllers with skewed input and output fuzzy sets.

    PubMed

    Mohan, B M; Sinha, Arpita

    2008-07-01

    This paper unveils mathematical models for fuzzy PI/PD controllers which employ two skewed fuzzy sets for each of the two-input variables and three skewed fuzzy sets for the output variable. The basic constituents of these models are Gamma-type and L-type membership functions for each input, trapezoidal/triangular membership functions for output, intersection/algebraic product triangular norm, maximum/drastic sum triangular conorm, Mamdani minimum/Larsen product/drastic product inference method, and center of sums defuzzification method. The existing simplest fuzzy PI/PD controller structures derived via symmetrical fuzzy sets become special cases of the mathematical models revealed in this paper. Finally, a numerical example along with its simulation results are included to demonstrate the effectiveness of the simplest fuzzy PI controllers.

  15. a New Model for Fuzzy Personalized Route Planning Using Fuzzy Linguistic Preference Relation

    NASA Astrophysics Data System (ADS)

    Nadi, S.; Houshyaripour, A. H.

    2017-09-01

    This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user's ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user's preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user's preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.

  16. Emergent fuzzy geometry and fuzzy physics in four dimensions

    NASA Astrophysics Data System (ADS)

    Ydri, Badis; Rouag, Ahlam; Ramda, Khaled

    2017-03-01

    A detailed Monte Carlo calculation of the phase diagram of bosonic mass-deformed IKKT Yang-Mills matrix models in three and six dimensions with quartic mass deformations is given. Background emergent fuzzy geometries in two and four dimensions are observed with a fluctuation given by a noncommutative U (1) gauge theory very weakly coupled to normal scalar fields. The geometry, which is determined dynamically, is given by the fuzzy spheres SN2 and SN2 × SN2 respectively. The three and six matrix models are effectively in the same universality class. For example, in two dimensions the geometry is completely stable, whereas in four dimensions the geometry is stable only in the limit M ⟶ ∞, where M is the mass of the normal fluctuations. The behaviors of the eigenvalue distribution in the two theories are also different. We also sketch how we can obtain a stable fuzzy four-sphere SN2 × SN2 in the large N limit for all values of M as well as models of topology change in which the transition between spheres of different dimensions is observed. The stable fuzzy spheres in two and four dimensions act precisely as regulators which is the original goal of fuzzy geometry and fuzzy physics. Fuzzy physics and fuzzy field theory on these spaces are briefly discussed.

  17. Evolutionary fuzzy modeling human diagnostic decisions.

    PubMed

    Peña-Reyes, Carlos Andrés

    2004-05-01

    Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

  18. Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.

    PubMed

    Shen, Qikun; Shi, Peng; Shi, Yan

    2016-12-01

    In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

  19. Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

    PubMed

    Zhang, Jin-Xi; Yang, Guang-Hong

    2018-05-01

    This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

  20. Modeling and simulation of evacuation behavior using fuzzy logic in a goal finding application

    NASA Astrophysics Data System (ADS)

    Sharma, Sharad; Ogunlana, Kola; Sree, Swetha

    2016-05-01

    Modeling and simulation has been widely used as a training and educational tool for depicting different evacuation strategies and damage control decisions during evacuation. However, there are few simulation environments that can include human behavior with low to high levels of fidelity. It is well known that crowd stampede induced by panic leads to fatalities as people are crushed or trampled. Our proposed goal finding application can be used to model situations that are difficult to test in real-life due to safety considerations. It is able to include agent characteristics and behaviors. Findings of this model are very encouraging as agents are able to assume various roles to utilize fuzzy logic on the way to reaching their goals. Fuzzy logic is used to model stress, panic and the uncertainty of emotions. The fuzzy rules link these parts together while feeding into behavioral rules. The contributions of this paper lies in our approach of utilizing fuzzy logic to show learning and adaptive behavior of agents in a goal finding application. The proposed application will aid in running multiple evacuation drills for what-if scenarios by incorporating human behavioral characteristics that can scale from a room to building. Our results show that the inclusion of fuzzy attributes made the evacuation time of the agents closer to the real time drills.

  1. L∞-gain adaptive fuzzy fault accommodation control design for nonlinear time-delay systems.

    PubMed

    Wu, Huai-Ning; Qiang, Xiao-Hong; Guo, Lei

    2011-06-01

    In this paper, an adaptive fuzzy fault accommodation (FA) control design with a guaranteed L(∞)-gain performance is developed for a class of nonlinear time-delay systems with persistent bounded disturbances. Using the Lyapunov technique and the Razumikhin-type lemma, the existence condition of the L(∞) -gain adaptive fuzzy FA controllers is provided in terms of linear matrix inequalities (LMIs). In the proposed FA scheme, a fuzzy logic system is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown fault function; a continuous-state feedback control strategy is adopted for the control design to avoid the undesirable chattering phenomenon. The resulting FA controllers can ensure that every response of the closed-loop system is uniformly ultimately bounded with a guaranteed L(∞)-gain performance in the presence of a fault. Moreover, by the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(∞)-gain. Finally, the achieved simulation results on the FA control of a continuous stirred tank reactor (CSTR) show the effectiveness of the proposed design procedure.

  2. Takagi-Sugeno-Kang fuzzy models of the rainfall-runoff transformation

    NASA Astrophysics Data System (ADS)

    Jacquin, A. P.; Shamseldin, A. Y.

    2009-04-01

    Fuzzy inference systems, or fuzzy models, are non-linear models that describe the relation between the inputs and the output of a real system using a set of fuzzy IF-THEN rules. This study deals with the application of Takagi-Sugeno-Kang type fuzzy models to the development of rainfall-runoff models operating on a daily basis, using a system based approach. The models proposed are classified in two types, each intended to account for different kinds of dominant non-linear effects in the rainfall-runoff relationship. Fuzzy models type 1 are intended to incorporate the effect of changes in the prevailing soil moisture content, while fuzzy models type 2 address the phenomenon of seasonality. Each model type consists of five fuzzy models of increasing complexity; the most complex fuzzy model of each model type includes all the model components found in the remaining fuzzy models of the respective type. The models developed are applied to data of six catchments from different geographical locations and sizes. Model performance is evaluated in terms of two measures of goodness of fit, namely the Nash-Sutcliffe criterion and the index of volumetric fit. The results of the fuzzy models are compared with those of the Simple Linear Model, the Linear Perturbation Model and the Nearest Neighbour Linear Perturbation Model, which use similar input information. Overall, the results of this study indicate that Takagi-Sugeno-Kang fuzzy models are a suitable alternative for modelling the rainfall-runoff relationship. However, it is also observed that increasing the complexity of the model structure does not necessarily produce an improvement in the performance of the fuzzy models. The relative importance of the different model components in determining the model performance is evaluated through sensitivity analysis of the model parameters in the accompanying study presented in this meeting. Acknowledgements: We would like to express our gratitude to Prof. Kieran M. O'Connor from the

  3. Adaptive backstepping sliding mode control with fuzzy monitoring strategy for a kind of mechanical system.

    PubMed

    Song, Zhankui; Sun, Kaibiao

    2014-01-01

    A novel adaptive backstepping sliding mode control (ABSMC) law with fuzzy monitoring strategy is proposed for the tracking-control of a kind of nonlinear mechanical system. The proposed ABSMC scheme combining the sliding mode control and backstepping technique ensure that the occurrence of the sliding motion in finite-time and the trajectory of tracking-error converge to equilibrium point. To obtain a better perturbation rejection property, an adaptive control law is employed to compensate the lumped perturbation. Furthermore, we introduce fuzzy monitoring strategy to improve adaptive capacity and soften the control signal. The convergence and stability of the proposed control scheme are proved by using Lyaponov's method. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose

    NASA Astrophysics Data System (ADS)

    Nanaa, K.; Rahman, M. N. A.; Rizon, M.; Mohamad, F. S.; Mamat, M.

    2018-03-01

    Classifying human face based on race and gender is a vital process in face recognition. It contributes to an index database and eases 3D synthesis of the human face. Identifying race and gender based on intrinsic factor is problematic, which is more fitting to utilizing nonlinear model for estimating process. In this paper, we aim to estimate race and gender in varied head pose. For this purpose, we collect dataset from PICS and CAS-PEAL databases, detect the landmarks and rotate them to the frontal pose. After geometric distances are calculated, all of distance values will be normalized. Implementation is carried out by using Neural Network Model and Fuzzy Logic Model. These models are combined by using Adaptive Neuro-Fuzzy Model. The experimental results showed that the optimization of address fuzzy membership. Model gives a better assessment rate and found that estimating race contributing to a more accurate gender assessment.

  5. Fuzzy control of magnetic bearings

    NASA Technical Reports Server (NTRS)

    Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.

    1991-01-01

    The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.

  6. Fuzzy model-based servo and model following control for nonlinear systems.

    PubMed

    Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

    2009-12-01

    This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.

  7. Comment on “Based on interval type-2 adaptive fuzzy H∞ tracking controller for SISO time-delay nonlinear systems”

    NASA Astrophysics Data System (ADS)

    Pan, Yongping; Huang, Daoping

    2011-03-01

    In this comment, we point out the inappropriateness of Theorem 1 in the article [Tsung-Chih Lin, Mehdi Roopaei. Based on interval type-2 adaptive fuzzy H∞ tracking controller for SISO time-delay nonlinear systems. Commun Nonlinear Sci Numer Simulat 2010;15:4065-75]. For solving this problem, some formular mistakes are corrected and novel parameter adaptive laws of interval type-2 fuzzy neural network system are given.

  8. Fuzzy model-based observers for fault detection in CSTR.

    PubMed

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.

    PubMed

    Aqil, M; Kita, I; Yano, A; Nishiyama, S

    2006-01-01

    It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other

  10. Multistage Fuzzy Decision Making in Bilateral Negotiation with Finite Termination Times

    NASA Astrophysics Data System (ADS)

    Richter, Jan; Kowalczyk, Ryszard; Klusch, Matthias

    In this paper we model the negotiation process as a multistage fuzzy decision problem where the agents preferences are represented by a fuzzy goal and fuzzy constraints. The opponent is represented by a fuzzy Markov decision process in the form of offer-response patterns which enables utilization of limited and uncertain information, e.g. the characteristics of the concession behaviour. We show that we can obtain adaptive negotiation strategies by only using the negotiation threads of two past cases to create and update the fuzzy transition matrix. The experimental evaluation demonstrates that our approach is adaptive towards different negotiation behaviours and that the fuzzy representation of the preferences and the transition matrix allows for application in many scenarios where the available information, preferences and constraints are soft or imprecise.

  11. New agrophysics divisions: application of ANFIS, fuzzy indicator modeling, physic-technical bases of plant breeding, and materials based on humic acids (review)

    USDA-ARS?s Scientific Manuscript database

    This work is devoted to review the new scientific divisions that emerged in agrophysics in the last 10-15 years. Among them are the following: 1) application of Adaptive Neuro-Fuzzy Inference System (ANFIS), 2) development and application of fuzzy indicator modeling, 3) agrophysical and physic-tech...

  12. A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

    PubMed

    Chang, Chia-Wen; Tao, Chin-Wang

    2017-09-01

    This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

  13. Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

    NASA Astrophysics Data System (ADS)

    Lin, Tsung-Chih

    2010-12-01

    In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

  14. Modeling Research Project Risks with Fuzzy Maps

    ERIC Educational Resources Information Center

    Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana

    2009-01-01

    The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…

  15. A fuzzy logic approach to modeling a vehicle crash test

    NASA Astrophysics Data System (ADS)

    Pawlus, Witold; Karimi, Hamid Reza; Robbersmyr, Kjell G.

    2013-03-01

    This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehicle's kinematics. It is concluded which factors have influence on the accuracy of the fuzzy model's output and how they can be adjusted to improve the model's fidelity.

  16. A reduced-form intensity-based model under fuzzy environments

    NASA Astrophysics Data System (ADS)

    Wu, Liang; Zhuang, Yaming

    2015-05-01

    The external shocks and internal contagion are the important sources of default events. However, the external shocks and internal contagion effect on the company is not observed, we cannot get the accurate size of the shocks. The information of investors relative to the default process exhibits a certain fuzziness. Therefore, using randomness and fuzziness to study such problems as derivative pricing or default probability has practical needs. But the idea of fuzzifying credit risk models is little exploited, especially in a reduced-form model. This paper proposes a new default intensity model with fuzziness and presents a fuzzy default probability and default loss rate, and puts them into default debt and credit derivative pricing. Finally, the simulation analysis verifies the rationality of the model. Using fuzzy numbers and random analysis one can consider more uncertain sources in the default process of default and investors' subjective judgment on the financial markets in a variety of fuzzy reliability so as to broaden the scope of possible credit spreads.

  17. Adaptive Fuzzy Systems in Computational Intelligence

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

  18. Fuzzy Logic as a Tool for Assessing Students' Knowledge and Skills

    ERIC Educational Resources Information Center

    Voskoglou, Michael Gr.

    2013-01-01

    Fuzzy logic, which is based on fuzzy sets theory introduced by Zadeh in 1965, provides a rich and meaningful addition to standard logic. The applications which may be generated from or adapted to fuzzy logic are wide-ranging and provide the opportunity for modeling under conditions which are imprecisely defined. In this article we develop a fuzzy…

  19. Adaptive fuzzy-neural-network control for maglev transportation system.

    PubMed

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  20. Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

    PubMed Central

    Jhin, Changho; Hwang, Keum Taek

    2014-01-01

    Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

  1. Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

    NASA Astrophysics Data System (ADS)

    Wu, Zhengping; Wu, Hao

    With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

  2. A fuzzy mathematical model of West Java population with logistic growth model

    NASA Astrophysics Data System (ADS)

    Nurkholipah, N. S.; Amarti, Z.; Anggriani, N.; Supriatna, A. K.

    2018-03-01

    In this paper we develop a mathematics model of population growth in the West Java Province Indonesia. The model takes the form as a logistic differential equation. We parameterize the model using several triples of data, and choose the best triple which has the smallest Mean Absolute Percentage Error (MAPE). The resulting model is able to predict the historical data with a high accuracy and it also able to predict the future of population number. Predicting the future population is among the important factors that affect the consideration is preparing a good management for the population. Several experiment are done to look at the effect of impreciseness in the data. This is done by considering a fuzzy initial value to the crisp model assuming that the model propagates the fuzziness of the independent variable to the dependent variable. We assume here a triangle fuzzy number representing the impreciseness in the data. We found that the fuzziness may disappear in the long-term. Other scenarios also investigated, such as the effect of fuzzy parameters to the crisp initial value of the population. The solution of the model is obtained numerically using the fourth-order Runge-Kutta scheme.

  3. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

    PubMed

    Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin

    2017-10-01

    Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary

  4. Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

    PubMed

    Zhang, Jian-Hua; Xia, Jia-Jun; Garibaldi, Jonathan M; Groumpos, Petros P; Wang, Ru-Bin

    2017-06-01

    In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN

  5. Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

    PubMed

    de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

    2017-01-01

    Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

  6. Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

    NASA Astrophysics Data System (ADS)

    Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

    2016-10-01

    Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.

  7. Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling

    NASA Technical Reports Server (NTRS)

    Chiu, Stephen

    1996-01-01

    Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.

  8. Intelligent neural network and fuzzy logic control of industrial and power systems

    NASA Astrophysics Data System (ADS)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  9. Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.

    PubMed

    Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

    2012-08-01

    In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.

  10. Flatness-based adaptive fuzzy control of chaotic finance dynamics

    NASA Astrophysics Data System (ADS)

    Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

    2017-11-01

    A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.

  11. Application of fuzzy adaptive control to a MIMO nonlinear time-delay pump-valve system.

    PubMed

    Lai, Zhounian; Wu, Peng; Wu, Dazhuan

    2015-07-01

    In this paper, a control strategy to balance the reliability against efficiency is introduced to overcome the common off-design operation problem in pump-valve systems. The pump-valve system is a nonlinear multi-input-multi-output (MIMO) system with time delays which cannot be accurately measured but can be approximately modeled using Bernoulli Principle. A fuzzy adaptive controller is applied to approximate system parameters and achieve the control of delay-free model since the system model is inaccurate and the direct feedback linearization method cannot be applied. An extended Smith predictor is introduced to compensate time delays of the system using the inaccurate system model. The experiment is carried out to verify the effectiveness of the control strategy whose results show that the control performance is well achieved. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Design of fuzzy system by NNs and realization of adaptability

    NASA Technical Reports Server (NTRS)

    Takagi, Hideyuki

    1993-01-01

    The issue of designing and tuning fuzzy membership functions by neural networks (NN's) was started by NN-driven Fuzzy Reasoning in 1988. NN-driven fuzzy reasoning involves a NN embedded in the fuzzy system which generates membership values. In conventional fuzzy system design, the membership functions are hand-crafted by trial and error for each input variable. In contrast, NN-driven fuzzy reasoning considers several variables simultaneously and can design a multidimensional, nonlinear membership function for the entire subspace.

  13. Can fuzzy logic bring complex problems into focus? Modeling imprecise factors in environmental policy

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    McKone, Thomas E.; Deshpande, Ashok W.

    2004-06-14

    In modeling complex environmental problems, we often fail to make precise statements about inputs and outcome. In this case the fuzzy logic method native to the human mind provides a useful way to get at these problems. Fuzzy logic represents a significant change in both the approach to and outcome of environmental evaluations. Risk assessment is currently based on the implicit premise that probability theory provides the necessary and sufficient tools for dealing with uncertainty and variability. The key advantage of fuzzy methods is the way they reflect the human mind in its remarkable ability to store and process informationmore » which is consistently imprecise, uncertain, and resistant to classification. Our case study illustrates the ability of fuzzy logic to integrate statistical measurements with imprecise health goals. But we submit that fuzzy logic and probability theory are complementary and not competitive. In the world of soft computing, fuzzy logic has been widely used and has often been the ''smart'' behind smart machines. But it will require more effort and case studies to establish its niche in risk assessment or other types of impact assessment. Although we often hear complaints about ''bright lines,'' could we adapt to a system that relaxes these lines to fuzzy gradations? Would decision makers and the public accept expressions of water or air quality goals in linguistic terms with computed degrees of certainty? Resistance is likely. In many regions, such as the US and European Union, it is likely that both decision makers and members of the public are more comfortable with our current system in which government agencies avoid confronting uncertainties by setting guidelines that are crisp and often fail to communicate uncertainty. But some day perhaps a more comprehensive approach that includes exposure surveys, toxicological data, epidemiological studies coupled with fuzzy modeling will go a long way in resolving some of the conflict

  14. Construction of fuzzy spaces and their applications to matrix models

    NASA Astrophysics Data System (ADS)

    Abe, Yasuhiro

    Quantization of spacetime by means of finite dimensional matrices is the basic idea of fuzzy spaces. There remains an issue of quantizing time, however, the idea is simple and it provides an interesting interplay of various ideas in mathematics and physics. Shedding some light on such an interplay is the main theme of this dissertation. The dissertation roughly separates into two parts. In the first part, we consider rather mathematical aspects of fuzzy spaces, namely, their construction. We begin with a review of construction of fuzzy complex projective spaces CP k (k = 1, 2, · · ·) in relation to geometric quantization. This construction facilitates defining symbols and star products on fuzzy CPk. Algebraic construction of fuzzy CPk is also discussed. We then present construction of fuzzy S 4, utilizing the fact that CP3 is an S2 bundle over S4. Fuzzy S4 is obtained by imposing an additional algebraic constraint on fuzzy CP3. Consequently it is proposed that coordinates on fuzzy S4 are described by certain block-diagonal matrices. It is also found that fuzzy S8 can analogously be constructed. In the second part of this dissertation, we consider applications of fuzzy spaces to physics. We first consider theories of gravity on fuzzy spaces, anticipating that they may offer a novel way of regularizing spacetime dynamics. We obtain actions for gravity on fuzzy S2 and on fuzzy CP3 in terms of finite dimensional matrices. Application to M(atrix) theory is also discussed. With an introduction of extra potentials to the theory, we show that it also has new brane solutions whose transverse directions are described by fuzzy S 4 and fuzzy CP3. The extra potentials can be considered as fuzzy versions of differential forms or fluxes, which enable us to discuss compactification models of M(atrix) theory. In particular, compactification down to fuzzy S4 is discussed and a realistic matrix model of M-theory in four-dimensions is proposed.

  15. Fuzzy object models for newborn brain MR image segmentation

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Udupa, Jayaram K.

    2013-03-01

    Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

  16. Approximation abilities of neuro-fuzzy networks

    NASA Astrophysics Data System (ADS)

    Mrówczyńska, Maria

    2010-01-01

    The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.

  17. Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.

    PubMed

    Perendeci, Altinay; Arslan, Sever; Tanyolaç, Abdurrahman; Celebi, Serdar S

    2009-10-01

    A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

  18. Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

    NASA Astrophysics Data System (ADS)

    Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

    2011-04-01

    Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

  19. Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

    NASA Astrophysics Data System (ADS)

    Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

    2010-10-01

    The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

  20. Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers

    USGS Publications Warehouse

    Hong, Y.-S.; Rosen, Michael R.; Reeves, R.R.

    2002-01-01

    In an urban fractured-rock aquifer in the Mt. Eden area of Auckland, New Zealand, disposal of storm water is via "soakholes" drilled directly into the top of the fractured basalt rock. The dynamic response of the groundwater level due to the storm water infiltration shows characteristics of a strongly time-varying system. A dynamic fuzzy modeling approach, which is based on multiple local models that are weighted using fuzzy membership functions, has been developed to identify and predict groundwater level fluctuations caused by storm water infiltration. The dynamic fuzzy model is initialized by the fuzzy clustering algorithm and optimized by the gradient-descent algorithm in order to effectively derive the multiple local models-each of which is associated with a locally valid model that represents the groundwater level state as a response to different intensities of rainfall events. The results have shown that even if the number of fuzzy local models derived is small, the fuzzy modeling approach developed provides good prediction results despite the highly time-varying nature of this urban fractured-rock aquifer system. Further, it allows interpretable representations of the dynamic behavior of the groundwater system due to storm water infiltration.

  1. Fuzzy adaptive strong tracking scaled unscented Kalman filter for initial alignment of large misalignment angles

    NASA Astrophysics Data System (ADS)

    Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui

    2016-07-01

    In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.

  2. A hybrid learning method for constructing compact rule-based fuzzy models.

    PubMed

    Zhao, Wanqing; Niu, Qun; Li, Kang; Irwin, George W

    2013-12-01

    The Takagi–Sugeno–Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature.

  3. Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

    NASA Astrophysics Data System (ADS)

    Vellingiri, Govindaraj; Jayabalan, Ramesh

    2018-03-01

    Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.

  4. A fuzzy inventory model with acceptable shortage using graded mean integration value method

    NASA Astrophysics Data System (ADS)

    Saranya, R.; Varadarajan, R.

    2018-04-01

    In many inventory models uncertainty is due to fuzziness and fuzziness is the closed possible approach to reality. In this paper, we proposed a fuzzy inventory model with acceptable shortage which is completely backlogged. We fuzzily the carrying cost, backorder cost and ordering cost using Triangular and Trapezoidal fuzzy numbers to obtain the fuzzy total cost. The purpose of our study is to defuzzify the total profit function by Graded Mean Integration Value Method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models.

  5. Fuzzy Model-based Pitch Stabilization and Wing Vibration Suppression of Flexible Wing Aircraft.

    NASA Technical Reports Server (NTRS)

    Ayoubi, Mohammad A.; Swei, Sean Shan-Min; Nguyen, Nhan T.

    2014-01-01

    This paper presents a fuzzy nonlinear controller to regulate the longitudinal dynamics of an aircraft and suppress the bending and torsional vibrations of its flexible wings. The fuzzy controller utilizes full-state feedback with input constraint. First, the Takagi-Sugeno fuzzy linear model is developed which approximates the coupled aeroelastic aircraft model. Then, based on the fuzzy linear model, a fuzzy controller is developed to utilize a full-state feedback and stabilize the system while it satisfies the control input constraint. Linear matrix inequality (LMI) techniques are employed to solve the fuzzy control problem. Finally, the performance of the proposed controller is demonstrated on the NASA Generic Transport Model (GTM).

  6. Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

    PubMed Central

    Yang, Jie; Kasabov, Nikola

    2017-01-01

    Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect. PMID:28744464

  7. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

    PubMed

    Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

    2016-05-09

    In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

  8. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

    PubMed Central

    Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

    2016-01-01

    In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

  9. Direct adaptive fuzzy control of a translating piezoelectric flexible manipulator driven by a pneumatic rodless cylinder

    NASA Astrophysics Data System (ADS)

    Qiu, Zhi-cheng; Wang, Bin; Zhang, Xian-min; Han, Jian-da

    2013-04-01

    This study presents a novel translating piezoelectric flexible manipulator driven by a rodless cylinder. Simultaneous positioning control and vibration suppression of the flexible manipulator is accomplished by using a hybrid driving scheme composed of the pneumatic cylinder and a piezoelectric actuator. Pulse code modulation (PCM) method is utilized for the cylinder. First, the system dynamics model is derived, and its standard multiple input multiple output (MIMO) state-space representation is provided. Second, a composite proportional derivative (PD) control algorithms and a direct adaptive fuzzy control method are designed for the MIMO system. Also, a time delay compensation algorithm, bandstop and low-pass filters are utilized, under consideration of the control hysteresis and the caused high-frequency modal vibration due to the long stroke of the cylinder, gas compression and nonlinear factors of the pneumatic system. The convergence of the closed loop system is analyzed. Finally, experimental apparatus is constructed and experiments are conducted. The effectiveness of the designed controllers and the hybrid driving scheme is verified through simulation and experimental comparison studies. The numerical simulation and experimental results demonstrate that the proposed system scheme of employing the pneumatic drive and piezoelectric actuator can suppress the vibration and achieve the desired positioning location simultaneously. Furthermore, the adopted adaptive fuzzy control algorithms can significantly enhance the control performance.

  10. Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

    PubMed

    Lam, H K

    2012-02-01

    This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

  11. Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

    PubMed Central

    Liu, Fei; Heiner, Monika; Yang, Ming

    2016-01-01

    Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. PMID:26910830

  12. Robust fuzzy output feedback controller for affine nonlinear systems via T-S fuzzy bilinear model: CSTR benchmark.

    PubMed

    Hamdy, M; Hamdan, I

    2015-07-01

    In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping

    NASA Astrophysics Data System (ADS)

    Park, Inhye; Choi, Jaewon; Jin Lee, Moung; Lee, Saro

    2012-11-01

    We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.

  14. Multitask TSK fuzzy system modeling by mining intertask common hidden structure.

    PubMed

    Jiang, Yizhang; Chung, Fu-Lai; Ishibuchi, Hisao; Deng, Zhaohong; Wang, Shitong

    2015-03-01

    The classical fuzzy system modeling methods implicitly assume data generated from a single task, which is essentially not in accordance with many practical scenarios where data can be acquired from the perspective of multiple tasks. Although one can build an individual fuzzy system model for each task, the result indeed tells us that the individual modeling approach will get poor generalization ability due to ignoring the intertask hidden correlation. In order to circumvent this shortcoming, we consider a general framework for preserving the independent information among different tasks and mining hidden correlation information among all tasks in multitask fuzzy modeling. In this framework, a low-dimensional subspace (structure) is assumed to be shared among all tasks and hence be the hidden correlation information among all tasks. Under this framework, a multitask Takagi-Sugeno-Kang (TSK) fuzzy system model called MTCS-TSK-FS (TSK-FS for multiple tasks with common hidden structure), based on the classical L2-norm TSK fuzzy system, is proposed in this paper. The proposed model can not only take advantage of independent sample information from the original space for each task, but also effectively use the intertask common hidden structure among multiple tasks to enhance the generalization performance of the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multitask fuzzy system model in multitask regression learning scenarios.

  15. Hybrid modeling of nitrate fate in large catchments using fuzzy-rules

    NASA Astrophysics Data System (ADS)

    van der Heijden, Sven; Haberlandt, Uwe

    2010-05-01

    Especially for nutrient balance simulations, physically based ecohydrological modeling needs an abundance of measured data and model parameters, which for large catchments all too often are not available in sufficient spatial or temporal resolution or are simply unknown. For efficient large-scale studies it is thus beneficial to have methods at one's disposal which are parsimonious concerning the number of model parameters and the necessary input data. One such method is fuzzy-rule based modeling, which compared to other machine-learning techniques has the advantages to produce models (the fuzzy-rules) which are physically interpretable to a certain extent, and to allow the explicit introduction of expert knowledge through pre-defined rules. The study focuses on the application of fuzzy-rule based modeling for nitrate simulation in large catchments, in particular concerning decision support. Fuzzy-rule based modeling enables the generation of simple, efficient, easily understandable models with nevertheless satisfactory accuracy for problems of decision support. The chosen approach encompasses a hybrid metamodeling, which includes the generation of fuzzy-rules with data originating from physically based models as well as a coupling with a physically based water balance model. For the generation of the needed training data and also as coupled water balance model the ecohydrological model SWAT is employed. The conceptual model divides the nitrate pathway into three parts. The first fuzzy-module calculates nitrate leaching with the percolating water from soil surface to groundwater, the second module simulates groundwater passage, and the final module replaces the in-stream processes. The aim of this modularization is to create flexibility for using each of the modules on its own, for changing or completely replacing it. For fuzzy-rule based modeling this can explicitly mean that the re-training of one of the modules with newly available data will be possible without

  16. Fuzzy Naive Bayesian model for medical diagnostic decision support.

    PubMed

    Wagholikar, Kavishwar B; Vijayraghavan, Sundararajan; Deshpande, Ashok W

    2009-01-01

    This work relates to the development of computational algorithms to provide decision support to physicians. The authors propose a Fuzzy Naive Bayesian (FNB) model for medical diagnosis, which extends the Fuzzy Bayesian approach proposed by Okuda. A physician's interview based method is described to define a orthogonal fuzzy symptom information system, required to apply the model. For the purpose of elaboration and elicitation of characteristics, the algorithm is applied to a simple simulated dataset, and compared with conventional Naive Bayes (NB) approach. As a preliminary evaluation of FNB in real world scenario, the comparison is repeated on a real fuzzy dataset of 81 patients diagnosed with infectious diseases. The case study on simulated dataset elucidates that FNB can be optimal over NB for diagnosing patients with imprecise-fuzzy information, on account of the following characteristics - 1) it can model the information that, values of some attributes are semantically closer than values of other attributes, and 2) it offers a mechanism to temper exaggerations in patient information. Although the algorithm requires precise training data, its utility for fuzzy training data is argued for. This is supported by the case study on infectious disease dataset, which indicates optimality of FNB over NB for the infectious disease domain. Further case studies on large datasets are required to establish utility of FNB.

  17. Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

    PubMed

    Mathur, Neha; Glesk, Ivan; Buis, Arjan

    2016-10-01

    Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  18. Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

    PubMed

    Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

    2014-12-01

    This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

  19. An Interval Type-2 Fuzzy Multiple Echelon Supply Chain Model

    NASA Astrophysics Data System (ADS)

    Miller, Simon; John, Robert

    Planning resources for a supply chain is a major factor determining its success or failure. In this paper we build on previous work introducing an Interval Type-2 Fuzzy Logic model of a multiple echelon supply chain. It is believed that the additional degree of uncertainty provided by Interval Type-2 Fuzzy Logic will allow for better representation of the uncertainty and vagueness present in resource planning models. First, the subject of Supply Chain Management is introduced, then some background is given on related work using Type-1 Fuzzy Logic. A description of the Interval Type-2 Fuzzy model is given, and a test scenario detailed. A Genetic Algorithm uses the model to search for a near-optimal plan for the scenario. A discussion of the results follows, along with conclusions and details of intended further work.

  20. A NOISE ADAPTIVE FUZZY EQUALIZATION METHOD FOR PROCESSING SOLAR EXTREME ULTRAVIOLET IMAGES

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Druckmueller, M., E-mail: druckmuller@fme.vutbr.cz

    A new image enhancement tool ideally suited for the visualization of fine structures in extreme ultraviolet images of the corona is presented in this paper. The Noise Adaptive Fuzzy Equalization method is particularly suited for the exceptionally high dynamic range images from the Atmospheric Imaging Assembly instrument on the Solar Dynamics Observatory. This method produces artifact-free images and gives significantly better results than methods based on convolution or Fourier transform which are often used for that purpose.

  1. Towards autonomous fuzzy control

    NASA Technical Reports Server (NTRS)

    Shenoi, Sujeet; Ramer, Arthur

    1993-01-01

    The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous self-organizing controllers employing system-independent control meta-knowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control actions give rise to adequate performance characteristics but could rapidly and autonomously initiate real-time adaptation whenever its performance degrades. Such an autonomous fuzzy controller would have immense practical value. It could accommodate individual variations in system characteristics and also compensate for degradations in system characteristics caused by wear and tear. It could also potentially deal with black-box systems and control scenarios. On-going research in autonomous fuzzy control is reported. The ultimate research objective is to develop robust and relatively inexpensive autonomous fuzzy control hardware suitable for use in real time environments.

  2. Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS.

    PubMed

    Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu

    2014-09-01

    This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. Copyright © 2014. Published by Elsevier Ltd.

  3. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

    NASA Astrophysics Data System (ADS)

    Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

    2014-03-01

    This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

  4. Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Yegireddi, Satyanarayana; Uday Bhaskar, G.

    2009-01-01

    Different parameters obtained through well-logging geophysical sensors such as SP, resistivity, gamma-gamma, neutron, natural gamma and acoustic, help in identification of strata and estimation of the physical, electrical and acoustical properties of the subsurface lithology. Strong and conspicuous changes in some of the log parameters associated with any particular stratigraphy formation, are function of its composition, physical properties and help in classification. However some substrata show moderate values in respective log parameters and make difficult to identify or assess the type of strata, if we go by the standard variability ranges of any log parameters and visual inspection. The complexity increases further with more number of sensors involved. An attempt is made to identify the type of stratigraphy from borehole geophysical log data using a combined approach of neural networks and fuzzy logic, known as Adaptive Neuro-Fuzzy Inference System. A model is built based on a few data sets (geophysical logs) of known stratigraphy of in coal areas of Kothagudem, Godavari basin and further the network model is used as test model to infer the lithology of a borehole from their geophysical logs, not used in simulation. The results are very encouraging and the model is able to decipher even thin cola seams and other strata from borehole geophysical logs. The model can be further modified to assess the physical properties of the strata, if the corresponding ground truth is made available for simulation.

  5. Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Luo, Shaohua

    This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaosmore » of PMSM and show the effectiveness and robustness of the proposed method.« less

  6. Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

    PubMed

    Luo, Shaohua

    2014-09-01

    This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

  7. A Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics

    DTIC Science & Technology

    2001-04-05

    Margin (ADM) and (ii) Fault Detection Margin (FDM). Key Words: ANFIS, Engine Health Monitoring , Gas Path Analysis, and Stochastic Analysis Adaptive Network...The paper illustrates the application of a hybrid Stochastic- Fuzzy -Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both...operational history monitored on-line by the engine health management (EHM) system. To capture the complex functional relationships between different

  8. Character recognition using a neural network model with fuzzy representation

    NASA Technical Reports Server (NTRS)

    Tavakoli, Nassrin; Seniw, David

    1992-01-01

    The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

  9. Transmogrifying fuzzy vortices

    NASA Astrophysics Data System (ADS)

    Murugan, Jeff; Millner, Antony

    2004-04-01

    We show that the construction of vortex solitons of the noncommutative abelian-Higgs model can be extended to a critically coupled gauged linear sigma model with Fayet-Illiopolous D-terms. Like its commutative counterpart, this fuzzy linear sigma model has a rich spectrum of BPS solutions. We offer an explicit construction of the degree-k static semilocal vortex and study in some detail the infinite coupling limit in which it descends to a degree-k Bbb CBbb PkN instanton. This relation between the fuzzy vortex and noncommutative lump is used to suggest an interpretation of the noncommutative sigma model soliton as tilted D-strings stretched between an NS5-brane and a stack of D3-branes in type-IIB superstring theory.

  10. Differential flatness properties and adaptive control of the hypothalamic-pituitary-adrenal axis model

    NASA Astrophysics Data System (ADS)

    Rigatos, Gerasimos

    2016-12-01

    It is shown that the model of the hypothalamic-pituitary-adrenal gland axis is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the state variables (CRH - corticotropin releasing hormone, adenocortocotropic hormone - ACTH, cortisol) of the hypothalamic-pituitary-adrenal gland axis model converge to the desirable levels (setpoints). This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The performed Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be succeeded.

  11. Fuzzy multiobjective models for optimal operation of a hydropower system

    NASA Astrophysics Data System (ADS)

    Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

    2013-06-01

    Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

  12. A fuzzy chance-constrained programming model with type 1 and type 2 fuzzy sets for solid waste management under uncertainty

    NASA Astrophysics Data System (ADS)

    Ma, Xiaolin; Ma, Chi; Wan, Zhifang; Wang, Kewei

    2017-06-01

    Effective management of municipal solid waste (MSW) is critical for urban planning and development. This study aims to develop an integrated type 1 and type 2 fuzzy sets chance-constrained programming (ITFCCP) model for tackling regional MSW management problem under a fuzzy environment, where waste generation amounts are supposed to be type 2 fuzzy variables and treated capacities of facilities are assumed to be type 1 fuzzy variables. The evaluation and expression of uncertainty overcome the drawbacks in describing fuzzy possibility distributions as oversimplified forms. The fuzzy constraints are converted to their crisp equivalents through chance-constrained programming under the same or different confidence levels. Regional waste management of the City of Dalian, China, was used as a case study for demonstration. The solutions under various confidence levels reflect the trade-off between system economy and reliability. It is concluded that the ITFCCP model is capable of helping decision makers to generate reasonable waste-allocation alternatives under uncertainties.

  13. Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.

    PubMed

    Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha

    2015-01-01

    This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  14. Discrimination of Human Forearm Motions on the Basis of Myoelectric Signals by Using Adaptive Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Kiso, Atsushi; Seki, Hirokazu

    This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

  15. Improved fuzzy PID controller design using predictive functional control structure.

    PubMed

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Enhancing dissolved oxygen control using an on-line hybrid fuzzy-neural soft-sensing model-based control system in an anaerobic/anoxic/oxic process.

    PubMed

    Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Ma, Yongwen; Wang, Yan

    2013-12-01

    An on-line hybrid fuzzy-neural soft-sensing model-based control system was developed to optimize dissolved oxygen concentration in a bench-scale anaerobic/anoxic/oxic (A(2)/O) process. In order to improve the performance of the control system, a self-adapted fuzzy c-means clustering algorithm and adaptive network-based fuzzy inference system (ANFIS) models were employed. The proposed control system permits the on-line implementation of every operating strategy of the experimental system. A set of experiments involving variable hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO), and mixed-liquid return ratio (r) was carried out. Using the proposed system, the amount of COD in the effluent stabilized at the set-point and below. The improvement was achieved with optimum dissolved oxygen concentration because the performance of the treatment process was optimized using operating rules implemented in real time. The system allows various expert operational approaches to be deployed with the goal of minimizing organic substances in the outlet while using the minimum amount of energy.

  17. Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter.

    PubMed

    Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen

    2017-11-01

    In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Integration of Genetic Algorithms and Fuzzy Logic for Urban Growth Modeling

    NASA Astrophysics Data System (ADS)

    Foroutan, E.; Delavar, M. R.; Araabi, B. N.

    2012-07-01

    Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.

  19. Reference set design for relational modeling of fuzzy systems

    NASA Astrophysics Data System (ADS)

    Lapohos, Tibor; Buchal, Ralph O.

    1994-10-01

    One of the keys to the successful relational modeling of fuzzy systems is the proper design of fuzzy reference sets. This has been discussed throughout the literature. In the frame of modeling a stochastic system, we analyze the problem numerically. First, we briefly describe the relational model and present the performance of the modeling in the most trivial case: the reference sets are triangle shaped. Next, we present a known fuzzy reference set generator algorithm (FRSGA) which is based on the fuzzy c-means (Fc-M) clustering algorithm. In the second section of this chapter we improve the previous FRSGA by adding a constraint to the Fc-M algorithm (modified Fc-M or MFc-M): two cluster centers are forced to coincide with the domain limits. This is needed to obtain properly shaped extreme linguistic reference values. We apply this algorithm to uniformly discretized domains of the variables involved. The fuzziness of the reference sets produced by both Fc-M and MFc-M is determined by a parameter, which in our experiments is modified iteratively. Each time, a new model is created and its performance analyzed. For certain algorithm parameter values both of these two algorithms have shortcomings. To eliminate the drawbacks of these two approaches, we develop a completely new generator algorithm for reference sets which we call Polyline. This algorithm and its performance are described in the last section. In all three cases, the modeling is performed for a variety of operators used in the inference engine and two defuzzification methods. Therefore our results depend neither on the system model order nor the experimental setup.

  20. A Stochastic Dynamic Programming Model With Fuzzy Storage States Applied to Reservoir Operation Optimization

    NASA Astrophysics Data System (ADS)

    Mousavi, Seyed Jamshid; Mahdizadeh, Kourosh; Afshar, Abbas

    2004-08-01

    Application of stochastic dynamic programming (SDP) models to reservoir optimization calls for state variables discretization. As an important variable discretization of reservoir storage volume has a pronounced effect on the computational efforts. The error caused by storage volume discretization is examined by considering it as a fuzzy state variable. In this approach, the point-to-point transitions between storage volumes at the beginning and end of each period are replaced by transitions between storage intervals. This is achieved by using fuzzy arithmetic operations with fuzzy numbers. In this approach, instead of aggregating single-valued crisp numbers, the membership functions of fuzzy numbers are combined. Running a simulated model with optimal release policies derived from fuzzy and non-fuzzy SDP models shows that a fuzzy SDP with a coarse discretization scheme performs as well as a classical SDP having much finer discretized space. It is believed that this advantage in the fuzzy SDP model is due to the smooth transitions between storage intervals which benefit from soft boundaries.

  1. Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

    NASA Astrophysics Data System (ADS)

    Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

    Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

  2. A fuzzy logic approach to modeling the underground economy in Taiwan

    NASA Astrophysics Data System (ADS)

    Yu, Tiffany Hui-Kuang; Wang, David Han-Min; Chen, Su-Jane

    2006-04-01

    The size of the ‘underground economy’ (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective tax rate and the degree of government regulation, are used. The size of Taiwan's UE is scaled and compared with those of other models. Although our approach yields different estimates, similar patterns and leading are exhibited throughout the period. The advantage of applying fuzzy logic is twofold. First, it can avoid the complex calculations in conventional econometric models. Second, fuzzy rules with linguistic terms are easy for human to understand.

  3. Dynamic Fuzzy Model Development for a Drum-type Boiler-turbine Plant Through GK Clustering

    NASA Astrophysics Data System (ADS)

    Habbi, Ahcène; Zelmat, Mimoun

    2008-10-01

    This paper discusses a TS fuzzy model identification method for an industrial drum-type boiler plant using the GK fuzzy clustering approach. The fuzzy model is constructed from a set of input-output data that covers a wide operating range of the physical plant. The reference data is generated using a complex first-principle-based mathematical model that describes the key dynamical properties of the boiler-turbine dynamics. The proposed fuzzy model is derived by means of fuzzy clustering method with particular attention on structure flexibility and model interpretability issues. This may provide a basement of a new way to design model based control and diagnosis mechanisms for the complex nonlinear plant.

  4. Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

    PubMed

    Nikbakht, Roya; Bahrampour, Abbas

    2017-01-01

    Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

  5. Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

    NASA Astrophysics Data System (ADS)

    Du, Jiaoman; Yu, Lean; Li, Xiang

    2016-04-01

    Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

  6. Fuzzy Adaptive Control Design and Discretization for a Class of Nonlinear Uncertain Systems.

    PubMed

    Zhao, Xudong; Shi, Peng; Zheng, Xiaolong

    2016-06-01

    In this paper, tracking control problems are investigated for a class of uncertain nonlinear systems in lower triangular form. First, a state-feedback controller is designed by using adaptive backstepping technique and the universal approximation ability of fuzzy logic systems. During the design procedure, a developed method with less computation is proposed by constructing one maximum adaptive parameter. Furthermore, adaptive controllers with nonsymmetric dead-zone are also designed for the systems. Then, a sampled-data control scheme is presented to discretize the obtained continuous-time controller by using the forward Euler method. It is shown that both proposed continuous and discrete controllers can ensure that the system output tracks the target signal with a small bounded error and the other closed-loop signals remain bounded. Two simulation examples are presented to verify the effectiveness and applicability of the proposed new design techniques.

  7. Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

    NASA Technical Reports Server (NTRS)

    Douglas, Freddie; Bourgeois, Edit Kaminsky

    2005-01-01

    The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).

  8. Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

    NASA Astrophysics Data System (ADS)

    Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

    2012-04-01

    Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

  9. Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

    NASA Astrophysics Data System (ADS)

    Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng

    2006-12-01

    An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.

  10. Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

    PubMed Central

    Coelho, Antonio Augusto Rodrigues

    2016-01-01

    This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

  11. Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

    NASA Astrophysics Data System (ADS)

    Habbi, Hacene; Kidouche, Madjid; Kinnaert, Michel; Zelmat, Mimoun

    2011-04-01

    This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

  12. Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

    PubMed

    Shi, Wuxi; Luo, Rui; Li, Baoquan

    2017-01-01

    In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Fuzzy Behavior-Based Navigation for Planetary

    NASA Technical Reports Server (NTRS)

    Tunstel, Edward; Danny, Harrison; Lippincott, Tanya; Jamshidi, Mo

    1997-01-01

    Adaptive behavioral capabilities are necessary for robust rover navigation in unstructured and partially-mapped environments. A control approach is described which exploits the approximate reasoning capability of fuzzy logic to produce adaptive motion behavior. In particular, a behavior-based architecture for hierarchical fuzzy control of microrovers is presented. Its structure is described, as well as mechanisms of control decision-making which give rise to adaptive behavior. Control decisions for local navigation result from a consensus of recommendations offered only by behaviors that are applicable to current situations. Simulation predicts the navigation performance on a microrover in simplified Mars-analog terrain.

  14. Fuzzy control of power converters based on quasilinear modelling

    NASA Astrophysics Data System (ADS)

    Li, C. K.; Lee, W. L.; Chou, Y. W.

    1995-03-01

    Unlike feedback control by the fuzzy PID method, a new fuzzy control algorithm based on quasilinear modelling of the DC-DC converter is proposed. Investigation is carried out using a buck-boost converter. Simulation results demonstrated that the converter can be regulated with improved performance even when subjected to input disturbance and load variation.

  15. Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

    NASA Astrophysics Data System (ADS)

    Bouzaida, Sana; Sakly, Anis; M'Sahli, Faouzi

    2014-01-01

    This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.

  16. A mathematical programming method for formulating a fuzzy regression model based on distance criterion.

    PubMed

    Chen, Liang-Hsuan; Hsueh, Chan-Ching

    2007-06-01

    Fuzzy regression models are useful to investigate the relationship between explanatory and response variables with fuzzy observations. Different from previous studies, this correspondence proposes a mathematical programming method to construct a fuzzy regression model based on a distance criterion. The objective of the mathematical programming is to minimize the sum of distances between the estimated and observed responses on the X axis, such that the fuzzy regression model constructed has the minimal total estimation error in distance. Only several alpha-cuts of fuzzy observations are needed as inputs to the mathematical programming model; therefore, the applications are not restricted to triangular fuzzy numbers. Three examples, adopted in the previous studies, and a larger example, modified from the crisp case, are used to illustrate the performance of the proposed approach. The results indicate that the proposed model has better performance than those in the previous studies based on either distance criterion or Kim and Bishu's criterion. In addition, the efficiency and effectiveness for solving the larger example by the proposed model are also satisfactory.

  17. Hysteresis modeling and identification of a dielectric electro-active polymer actuator using an APSO-based nonlinear Preisach NARX fuzzy model

    NASA Astrophysics Data System (ADS)

    Truong, Bui Ngoc Minh; Nam, Doan Ngoc Chi; Ahn, Kyoung Kwan

    2013-09-01

    Dielectric electro-active polymer (DEAP) materials are attractive since they are low cost, lightweight and have a large deformation capability. They have no operating noise, very low electric power consumption and higher performance and efficiency than competing technologies. However, DEAP materials generally have strong hysteresis as well as uncertain and nonlinear characteristics. These disadvantages can limit the efficiency in the use of DEAP materials. To address these limitations, this research will present the combination of the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model-based adaptive particle swarm optimization (APSO) identification algorithm for modeling and identification of the nonlinear behavior of one typical type of DEAP actuator. Firstly, open loop input signals are applied to obtain nonlinear features and to investigate the responses of the DEAP actuator system. Then, a Preisach model can be combined with a dynamic NARX fuzzy structure to estimate the tip displacement of a DEAP actuator. To optimize all unknown parameters of the designed combination, an identification scheme based on a least squares method and an APSO algorithm is carried out. Finally, experimental validation research is carefully completed, and the effectiveness of the proposed model is evaluated by employing various input signals.

  18. A fuzzy goal programming model for biodiesel production

    NASA Astrophysics Data System (ADS)

    Lutero, D. S.; Pangue, EMU; Tubay, J. M.; Lubag, S. P.

    2016-02-01

    A fuzzy goal programming (FGP) model for biodiesel production in the Philippines was formulated with Coconut (Cocos nucifera) and Jatropha (Jatropha curcas) as sources of biodiesel. Objectives were maximization of feedstock production and overall revenue and, minimization of energy used in production and working capital for farming subject to biodiesel and non-biodiesel requirements, and availability of land, labor, water and machine time. All these objectives and constraints were assumed to be fuzzy. Model was tested for different sets of weights. Results for all sets of weights showed the same optimal allocation. Coconut alone can satisfy the biodiesel requirement of 2% per volume.

  19. Fuzzy linear model for production optimization of mining systems with multiple entities

    NASA Astrophysics Data System (ADS)

    Vujic, Slobodan; Benovic, Tomo; Miljanovic, Igor; Hudej, Marjan; Milutinovic, Aleksandar; Pavlovic, Petar

    2011-12-01

    Planning and production optimization within multiple mines or several work sites (entities) mining systems by using fuzzy linear programming (LP) was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

  20. Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

    NASA Astrophysics Data System (ADS)

    Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

    2014-09-01

    Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

  1. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Akhavan, P.; Karimi, M.; Pahlavani, P.

    2014-10-01

    Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  2. Spinning the fuzzy sphere

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Berenstein, David; Dzienkowski, Eric; Lashof-Regas, Robin

    Here, we construct various exact analytical solutions of the SO(3) BMN matrix model that correspond to rotating fuzzy spheres and rotating fuzzy tori. These are also solutions of Yang Mills theory compactified on a sphere times time and they are also translationally invariant solutions of the N = 1* field theory with a non-trivial chargedensity. The solutions we construct have a Ζ N symmetry, where N is the rank of the matrices. After an appropriate ansatz, we reduce the problem to solving a set of polynomial equations in 2N real variables. These equations have a discrete set of solutions formore » each value of the angular momentum. We study the phase structure of the solutions for various values of N . Also the continuum limit where N → ∞, where the problem reduces to finding periodic solutions of a set of coupled differential equations. We also study the topology change transition from the sphere to the torus.« less

  3. Spinning the fuzzy sphere

    DOE PAGES

    Berenstein, David; Dzienkowski, Eric; Lashof-Regas, Robin

    2015-08-27

    Here, we construct various exact analytical solutions of the SO(3) BMN matrix model that correspond to rotating fuzzy spheres and rotating fuzzy tori. These are also solutions of Yang Mills theory compactified on a sphere times time and they are also translationally invariant solutions of the N = 1* field theory with a non-trivial chargedensity. The solutions we construct have a Ζ N symmetry, where N is the rank of the matrices. After an appropriate ansatz, we reduce the problem to solving a set of polynomial equations in 2N real variables. These equations have a discrete set of solutions formore » each value of the angular momentum. We study the phase structure of the solutions for various values of N . Also the continuum limit where N → ∞, where the problem reduces to finding periodic solutions of a set of coupled differential equations. We also study the topology change transition from the sphere to the torus.« less

  4. Proposal for Classifying the Severity of Speech Disorder Using a Fuzzy Model in Accordance with the Implicational Model of Feature Complexity

    ERIC Educational Resources Information Center

    Brancalioni, Ana Rita; Magnago, Karine Faverzani; Keske-Soares, Marcia

    2012-01-01

    The objective of this study is to create a new proposal for classifying the severity of speech disorders using a fuzzy model in accordance with a linguistic model that represents the speech acquisition of Brazilian Portuguese. The fuzzy linguistic model was run in the MATLAB software fuzzy toolbox from a set of fuzzy rules, and it encompassed…

  5. Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

    PubMed

    Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

    2014-05-01

    This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Combinational Reasoning of Quantitative Fuzzy Topological Relations for Simple Fuzzy Regions

    PubMed Central

    Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi

    2015-01-01

    In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models. PMID:25775452

  7. Adaptive fuzzy PID control of hydraulic servo control system for large axial flow compressor

    NASA Astrophysics Data System (ADS)

    Wang, Yannian; Wu, Peizhi; Liu, Chengtao

    2017-09-01

    To improve the stability of the large axial compressor, an efficient and special intelligent hydraulic servo control system is designed and implemented. The adaptive fuzzy PID control algorithm is used to control the position of the hydraulic servo cylinder steadily, which overcomes the drawback that the PID parameters should be adjusted based on the different applications. The simulation and the test results show that the system has a better dynamic property and a stable state performance.

  8. Effect of diabetic neuropathy severity classified by a fuzzy model in muscle dynamics during gait

    PubMed Central

    2014-01-01

    Background Electromyography (EMG) alterations during gait, supposedly caused by diabetic sensorimotor polyneuropathy, are subtle and still inconsistent, due to difficulties in defining homogeneous experimental groups with a clear definition of disease stages. Since evaluating these patients involve many uncertainties, the use of a fuzzy model could enable a better discrimination among different stages of diabetic polyneuropathy and lead to a clarification of when changes in muscle activation start occurring. The aim of this study was to investigate EMG patterns during gait in diabetic individuals with different stages of DSP severity, classified by a fuzzy system. Methods 147 subjects were divided into a control group (n = 30) and four diabetic groups: absent (n = 43), mild (n = 30), moderate (n = 16), and severe (n = 28) neuropathy, classified by a fuzzy model. The EMG activity of the vastus lateralis, tibialis anterior, and gastrocnemius medialis were measured during gait. Temporal and relative magnitude variables were compared among groups using ANOVA tests. Results Muscle activity changes are present even before an established neural involvement, with delay in vastus lateralis peak and lower tibialis anterior relative magnitude. These alterations suggest an impaired ankle shock absorption mechanism, with compensation at the knee. This condition seems to be more pronounced in higher degrees of neuropathy, as there is an increased vastus lateralis activity in the mild and severe neuropathy groups. Tibialis anterior onset at terminal stance was anticipated in all diabetic groups; at higher degrees of neuropathy, the gastrocnemius medialis exhibited activity reduction and peak delay. Conclusion EMG alterations in the vastus lateralis and tibialis anterior occur even in the absence of diabetic neuropathy and in mild neuropathic subjects, seemingly causing changes in the shock absorption mechanisms at the heel strike. These changes increase with the onset of neural

  9. Improvement of the F-Perceptory Approach Through Management of Fuzzy Complex Geographic Objects

    NASA Astrophysics Data System (ADS)

    Khalfi, B.; de Runz, C.; Faiz, S.; Akdag, H.

    2015-08-01

    In the real world, data is imperfect and in various ways such as imprecision, vagueness, uncertainty, ambiguity and inconsistency. For geographic data, the fuzzy aspect is mainly manifested in time, space and the function of objects and is due to a lack of precision. Therefore, the researchers in the domain emphasize the importance of modeling data structures in GIS but also their lack of adaptation to fuzzy data. The F-Perceptory approachh manages the modeling of imperfect geographic information with UML. This management is essential to maintain faithfulness to reality and to better guide the user in his decision-making. However, this approach does not manage fuzzy complex geographic objects. The latter presents a multiple object with similar or different geographic shapes. So, in this paper, we propose to improve the F-Perceptory approach by proposing to handle fuzzy complex geographic objects modeling. In a second step, we propose its transformation to the UML modeling.

  10. New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

    ERIC Educational Resources Information Center

    Song, Qiang; Chissom, Brad S.

    Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a…

  11. Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data

    NASA Astrophysics Data System (ADS)

    Ameur, Mourad; Derras, Boumédiène; Zendagui, Djawed

    2018-03-01

    Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude ( Mw), the Joyner-Boore distance ( R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057-1085, 2014; Derras et al., in Earthquake Spectra 32:2027-2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated.

  12. Ground Motion Prediction Model Using Adaptive Neuro-Fuzzy Inference Systems: An Example Based on the NGA-West 2 Data

    NASA Astrophysics Data System (ADS)

    Ameur, Mourad; Derras, Boumédiène; Zendagui, Djawed

    2017-12-01

    Adaptive neuro-fuzzy inference systems (ANFIS) are used here to obtain the robust ground motion prediction model (GMPM). Avoiding a priori functional form, ANFIS provides fully data-driven predictive models. A large subset of the NGA-West2 database is used, including 2335 records from 580 sites and 137 earthquakes. Only shallow earthquakes and recordings corresponding to stations with measured V s30 properties are selected. Three basics input parameters are chosen: the moment magnitude (Mw), the Joyner-Boore distance (R JB) and V s30. ANFIS model output is the peak ground acceleration (PGA), peak ground velocity (PGV) and 5% damped pseudo-spectral acceleration (PSA) at periods from 0.01 to 4 s. A procedure similar to the random-effects approach is developed to provide between- and within-event standard deviations. The total standard deviation (SD) varies between [0.303 and 0.360] (log10 units) depending on the period. The ground motion predictions resulting from such simple three explanatory variables ANFIS models are shown to be comparable to the most recent NGA results (e.g., Boore et al., in Earthquake Spectra 30:1057-1085, 2014; Derras et al., in Earthquake Spectra 32:2027-2056, 2016). The main advantage of ANFIS compared to artificial neuronal network (ANN) is its simple and one-off topology: five layers. Our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude and amplification on soft soils. The ability to implement ANFIS model using an analytic equation and Excel is demonstrated.

  13. Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

    NASA Astrophysics Data System (ADS)

    Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

    2018-02-01

    This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

  14. Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

    PubMed

    Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

    2014-01-01

    Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

  15. Fuzzy control for closed-loop, patient-specific hypnosis in intraoperative patients: a simulation study.

    PubMed

    Moore, Brett L; Pyeatt, Larry D; Doufas, Anthony G

    2009-01-01

    Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzy controller demonstrated clinically acceptable performance, suggesting that further study is warranted.

  16. Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

    NASA Astrophysics Data System (ADS)

    Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

    2018-06-01

    To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.

  17. Higher spin gauge theory on fuzzy \\boldsymbol {S^4_N}

    NASA Astrophysics Data System (ADS)

    Sperling, Marcus; Steinacker, Harold C.

    2018-02-01

    We examine in detail the higher spin fields which arise on the basic fuzzy sphere S^4N in the semi-classical limit. The space of functions can be identified with functions on classical S 4 taking values in a higher spin algebra associated to \

  18. [Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

    PubMed

    Luo, Yi; Zhang, Tao; Li, Xiao-song

    2016-05-01

    To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

  19. Fuzzy Similarity and Fuzzy Inclusion Measures in Polyline Matching: A Case Study of Potential Streams Identification for Archaeological Modelling in GIS

    NASA Astrophysics Data System (ADS)

    Ďuračiová, Renata; Rášová, Alexandra; Lieskovský, Tibor

    2017-12-01

    When combining spatial data from various sources, it is often important to determine similarity or identity of spatial objects. Besides the differences in geometry, representations of spatial objects are inevitably more or less uncertain. Fuzzy set theory can be used to address both modelling of the spatial objects uncertainty and determining the identity, similarity, and inclusion of two sets as fuzzy identity, fuzzy similarity, and fuzzy inclusion. In this paper, we propose to use fuzzy measures to determine the similarity or identity of two uncertain spatial object representations in geographic information systems. Labelling the spatial objects by the degree of their similarity or inclusion measure makes the process of their identification more efficient. It reduces the need for a manual control. This leads to a more simple process of spatial datasets update from external data sources. We use this approach to get an accurate and correct representation of historical streams, which is derived from contemporary digital elevation model, i.e. we identify the segments that are similar to the streams depicted on historical maps.

  20. Fuzzy logic applications to control engineering

    NASA Astrophysics Data System (ADS)

    Langari, Reza

    1993-12-01

    This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

  1. Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

    NASA Astrophysics Data System (ADS)

    Roozegar, Mehdi; Mahjoob, Mohammad J.; Ayati, Moosa

    2017-05-01

    This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton-Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.

  2. Using fuzzy models in machining control system and assessment of sustainability

    NASA Astrophysics Data System (ADS)

    Grinek, A. V.; Boychuk, I. P.; Dantsevich, I. M.

    2018-03-01

    Description of the complex relationship of the optimum velocity with the temperature-strength state in the cutting zone for machining a fuzzy model is proposed. The fuzzy-logical conclusion allows determining the processing speed, which ensures effective, from the point of view of ensuring the quality of the surface layer, the temperature in the cutting zone and the maximum allowable cutting force. A scheme for stabilizing the temperature-strength state in the cutting zone using a nonlinear fuzzy PD–controller is proposed. The stability of the nonlinear system is estimated with the help of grapho–analytical realization of the method of harmonic balance and by modeling in MatLab.

  3. Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Kim, Chan Moon; Parnichkun, Manukid

    2017-11-01

    Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

  4. Evolving fuzzy rules in a learning classifier system

    NASA Technical Reports Server (NTRS)

    Valenzuela-Rendon, Manuel

    1993-01-01

    The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

  5. Fuzzy logic controller optimization

    DOEpatents

    Sepe, Jr., Raymond B; Miller, John Michael

    2004-03-23

    A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

  6. Cloud E-Learning Service Strategies for Improving E-Learning Innovation Performance in a Fuzzy Environment by Using a New Hybrid Fuzzy Multiple Attribute Decision-Making Model

    ERIC Educational Resources Information Center

    Su, Chiu Hung; Tzeng, Gwo-Hshiung; Hu, Shu-Kung

    2016-01-01

    The purpose of this study was to address this problem by applying a new hybrid fuzzy multiple criteria decision-making model including (a) using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to construct the fuzzy scope influential network relationship map (FSINRM) and determine the fuzzy influential weights of the…

  7. A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

    PubMed

    Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

    2018-01-02

    In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

  8. Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters

    NASA Astrophysics Data System (ADS)

    Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

    Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86

  9. Fuzzy branching temporal logic.

    PubMed

    Moon, Seong-ick; Lee, Kwang H; Lee, Doheon

    2004-04-01

    Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.

  10. Consumer preference models: fuzzy theory approach

    NASA Astrophysics Data System (ADS)

    Turksen, I. B.; Wilson, I. A.

    1993-12-01

    Consumer preference models are widely used in new product design, marketing management, pricing and market segmentation. The purpose of this article is to develop and test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation) and how much to make (market share prediction).

  11. Fuzzy Logic Engine

    NASA Technical Reports Server (NTRS)

    Howard, Ayanna

    2005-01-01

    The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.

  12. Simulation analysis of adaptive cruise prediction control

    NASA Astrophysics Data System (ADS)

    Zhang, Li; Cui, Sheng Min

    2017-09-01

    Predictive control is suitable for multi-variable and multi-constraint system control.In order to discuss the effect of predictive control on the vehicle longitudinal motion, this paper establishes the expected spacing model by combining variable pitch spacing and the of safety distance strategy. The model predictive control theory and the optimization method based on secondary planning are designed to obtain and track the best expected acceleration trajectory quickly. Simulation models are established including predictive and adaptive fuzzy control. Simulation results show that predictive control can realize the basic function of the system while ensuring the safety. The application of predictive and fuzzy adaptive algorithm in cruise condition indicates that the predictive control effect is better.

  13. Study on Fuzzy Adaptive Fractional Order PIλDμ Control for Maglev Guiding System

    NASA Astrophysics Data System (ADS)

    Hu, Qing; Hu, Yuwei

    The mathematical model of the linear elevator maglev guiding system is analyzed in this paper. For the linear elevator needs strong stability and robustness to run, the integer order PID was expanded to the fractional order, in order to improve the steady state precision, rapidity and robustness of the system, enhance the accuracy of the parameter in fractional order PIλDμ controller, the fuzzy control is combined with the fractional order PIλDμ control, using the fuzzy logic achieves the parameters online adjustment. The simulations reveal that the system has faster response speed, higher tracking precision, and has stronger robustness to the disturbance.

  14. Formation of an internal model of environment dynamics during upper limb reaching movements: a fuzzy approach.

    PubMed

    MacDonald, Chad; Moussavi, Zahra; Sarkodie-Gyan, Thompson

    2007-01-01

    This paper presents the development and simulation of a fuzzy logic based learning mechanism to emulate human motor learning. In particular, fuzzy inference was used to develop an internal model of a novel dynamic environment experienced during planar reaching movements with the upper limb. A dynamic model of the human arm was developed and a fuzzy if-then rule base was created to relate trajectory movement and velocity errors to internal model update parameters. An experimental simulation was performed to compare the fuzzy system's performance with that of human subjects. It was found that the dynamic model behaved as expected, and the fuzzy learning mechanism created an internal model that was capable of opposing the environmental force field to regain a trajectory closely resembling the desired ideal.

  15. Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

    NASA Astrophysics Data System (ADS)

    Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

    2017-12-01

    We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

  16. Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

    PubMed Central

    Vahabi, Zahra; Kermani, Saeed

    2012-01-01

    Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

  17. Fuzzy-probabilistic model for risk assessment of radioactive material railway transportation.

    PubMed

    Avramenko, M; Bolyatko, V; Kosterev, V

    2005-01-01

    Transportation of radioactive materials is obviously accompanied by a certain risk. A model for risk assessment of emergency situations and terrorist attacks may be useful for choosing possible routes and for comparing the various defence strategies. In particular, risk assessment is crucial for safe transportation of excess weapons-grade plutonium arising from the removal of plutonium from military employment. A fuzzy-probabilistic model for risk assessment of railway transportation has been developed taking into account the different natures of risk-affecting parameters (probabilistic and not probabilistic but fuzzy). Fuzzy set theory methods as well as standard methods of probability theory have been used for quantitative risk assessment. Information-preserving transformations are applied to realise the correct aggregation of probabilistic and fuzzy parameters. Estimations have also been made of the inhalation doses resulting from possible accidents during plutonium transportation. The obtained data show the scale of possible consequences that may arise from plutonium transportation accidents.

  18. Combining fuzzy mathematics with fuzzy logic to solve business management problems

    NASA Astrophysics Data System (ADS)

    Vrba, Joseph A.

    1993-12-01

    Fuzzy logic technology has been applied to control problems with great success. Because of this, many observers fell that fuzzy logic is applicable only in the control arena. However, business management problems almost never deal with crisp values. Fuzzy systems technology--a combination of fuzzy logic, fuzzy mathematics and a graphical user interface--is a natural fit for developing software to assist in typical business activities such as planning, modeling and estimating. This presentation discusses how fuzzy logic systems can be extended through the application of fuzzy mathematics and the use of a graphical user interface to make the information contained in fuzzy numbers accessible to business managers. As demonstrated through examples from actual deployed systems, this fuzzy systems technology has been employed successfully to provide solutions to the complex real-world problems found in the business environment.

  19. Adaptive Neural Networks for Automatic Negotiation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

    The use of fuzzy logic and fuzzy neural networks has been found effective for the modelling of the uncertain relations between the parameters of a negotiation procedure. The problem with these configurations is that they are static, that is, any new knowledge from theory or experiment lead to the construction of entirely new models. To overcome this difficulty, we apply in this work, an adaptive neural topology to model the negotiation process. Finally a simple simulation is carried in order to test the new method.

  20. A Distributed Fuzzy Associative Classifier for Big Data.

    PubMed

    Segatori, Armando; Bechini, Alessio; Ducange, Pietro; Marcelloni, Francesco

    2017-09-19

    Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11,000,000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

  1. Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

    PubMed

    Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

    2007-05-01

    This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks.

  2. Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

    NASA Astrophysics Data System (ADS)

    Chen, Jiaxi; Li, Junmin

    2018-02-01

    In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

  3. Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

    NASA Astrophysics Data System (ADS)

    Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

    2017-03-01

    This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

  4. Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment.

    PubMed

    Bidargaddi, Niranjan P; Chetty, Madhu; Kamruzzaman, Joarder

    2008-06-01

    Profile hidden Markov models (HMMs) based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.

  5. Assessing urban adaptive capacity to climate change.

    PubMed

    Araya-Muñoz, Dahyann; Metzger, Marc J; Stuart, Neil; Wilson, A Meriwether W; Alvarez, Luis

    2016-12-01

    Despite the growing number of studies focusing on urban vulnerability to climate change, adaptive capacity, which is a key component of the IPCC definition of vulnerability, is rarely assessed quantitatively. We examine the capacity of adaptation in the Concepción Metropolitan Area, Chile. A flexible methodology based on spatial fuzzy modelling was developed to standardise and aggregate, through a stepwise approach, seventeen indicators derived from widely available census statistical data into an adaptive capacity index. The results indicate that all the municipalities in the CMA increased their level of adaptive capacity between 1992 and 2002. However, the relative differences between municipalities did not change significantly over the studied timeframe. Fuzzy overlay allowed us to standardise and to effectively aggregate indicators with differing ranges and granularities of attribute values into an overall index. It also provided a conceptually sound and reproducible means of exploring the interplay of many indicators that individually influence adaptive capacity. Furthermore, it captured the complex, aggregated and continued nature of the adaptive capacity, favouring to deal with gaps of data and knowledge associated with the concept of adaptive capacity. The resulting maps can help identify municipalities where adaptive capacity is weak and identify which components of adaptive capacity need strengthening. Identification of these capacity conditions can stimulate dialogue amongst policymakers and stakeholders regarding how to manage urban areas and how to prioritise resources for urban development in ways that can also improve adaptive capacity and thus reduce vulnerability to climate change. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Fuzzy Modal Control Applied to Smart Composite Structure

    NASA Astrophysics Data System (ADS)

    Koroishi, E. H.; Faria, A. W.; Lara-Molina, F. A.; Steffen, V., Jr.

    2015-07-01

    This paper proposes an active vibration control technique, which is based on Fuzzy Modal Control, as applied to a piezoelectric actuator bonded to a composite structure forming a so-called smart composite structure. Fuzzy Modal Controllers were found to be well adapted for controlling structures with nonlinear behavior, whose characteristics change considerably with respect to time. The smart composite structure was modelled by using a so called mixed theory. This theory uses a single equivalent layer for the discretization of the mechanical displacement field and a layerwise representation of the electrical field. Temperature effects are neglected. Due to numerical reasons it was necessary to reduce the size of the model of the smart composite structure so that the design of the controllers and the estimator could be performed. The role of the Kalman Estimator in the present contribution is to estimate the modal states of the system, which are used by the Fuzzy Modal controllers. Simulation results illustrate the effectiveness of the proposed vibration control methodology for composite structures.

  7. Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

    NASA Astrophysics Data System (ADS)

    Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

    2013-09-01

    This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

  8. A fuzzy rumor spreading model based on transmission capacity

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Xu, Jiuping; Wu, Yue

    This paper proposes a rumor spreading model that considers three main factors: the event importance, event ambiguity, and the publics critical sense, each of which are defined by decision makers using linguistic descriptions and then transformed into triangular fuzzy numbers. To calculate the resultant force of these three factors, the transmission capacity and a new parameter category with fuzzy variables are determined. A rumor spreading model is then proposed which has fuzzy parameters rather than the fixed parameters in traditional models. As the proposed model considers the comprehensive factors affecting rumors from three aspects rather than examining special factors from a particular aspect. The proposed rumor spreading model is tested using different parameters for several different conditions on BA networks and three special cases are simulated. The simulation results for all three cases suggested that events of low importance, those that are only clarifying facts, and those that are strongly critical do not result in rumors. Therefore, the model assessment results were proven to be in agreement with reality. Parameters for the model were then determined and applied to an analysis of the 7.23 Yong-Wen line major transportation accident (YWMTA). When the simulated data were compared with the real data from this accident, the results demonstrated that the interval for the rumor spreading key point in the model was accurate, and that the key point for the YWMTA rumor spread fell into the range estimated by the model.

  9. Fuzzy cellular automata models in immunology

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ahmed, E.

    1996-10-01

    The self-nonself character of antigens is considered to be fuzzy. The Chowdhury et al. cellular automata model is generalized accordingly. New steady states are found. The first corresponds to a below-normal help and suppression and is proposed to be related to autoimmune diseases. The second corresponds to a below-normal B-cell level.

  10. New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Bonakdari, Hossein; Zaji, Amir Hossein

    2018-03-01

    In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively.

  11. The consistency of positive fully fuzzy linear system

    NASA Astrophysics Data System (ADS)

    Malkawi, Ghassan O.; Alfifi, Hassan Y.

    2017-11-01

    In this paper, the consistency of fuzziness of positive solution of the n × n fully fuzzy linear system (P - FFLS) is studied based on its associated linear system (P - ALS). That can consist of the whole entries of triangular fuzzy numbers in a linear system without fuzzy operations. The nature of solution is differentiated in case of fuzzy solution, non-fuzzy solution and fuzzy non-positive solution. Moreover, the analysis reveals that the P - ALS is applicable to provide the set of infinite number of solutions. Numerical examples are presented to illustrate the proposed analysis.

  12. An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness.

    PubMed

    Nguyen, Huu-Tho; Dawal, Siti Zawiah Md; Nukman, Yusoff; Rifai, Achmad P; Aoyama, Hideki

    2016-01-01

    The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts' uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.

  13. An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

    PubMed Central

    Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; P. Rifai, Achmad; Aoyama, Hideki

    2016-01-01

    The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs. PMID:27070543

  14. Proposal for classifying the severity of speech disorder using a fuzzy model in accordance with the implicational model of feature complexity.

    PubMed

    Brancalioni, Ana Rita; Magnago, Karine Faverzani; Keske-Soares, Marcia

    2012-09-01

    The objective of this study is to create a new proposal for classifying the severity of speech disorders using a fuzzy model in accordance with a linguistic model that represents the speech acquisition of Brazilian Portuguese. The fuzzy linguistic model was run in the MATLAB software fuzzy toolbox from a set of fuzzy rules, and it encompassed three input variables: path routing, level of complexity and phoneme acquisition. The output was the Speech Disorder Severity Index, and it used the following fuzzy subsets: severe, moderate severe, mild moderate and mild. The proposal was used for 204 children with speech disorders who were monolingual speakers of Brazilian Portuguese. The fuzzy linguistic model provided the Speech Disorder Severity Index for all of the evaluated phonological systems in a fast and practical manner. It was then possible to classify the systems according to the severity of the speech disorder as severe, moderate severe, mild moderate and mild; the speech disorders could also be differentiated according to the severity index.

  15. Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

    PubMed

    Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

    2018-05-10

    In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Dynamic modeling and adaptive vibration suppression of a high-speed macro-micro manipulator

    NASA Astrophysics Data System (ADS)

    Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Fang, Sheng; Chen, Te-huan

    2018-05-01

    This paper presents a dynamic modeling and microscopic vibration suppression for a flexible macro-micro manipulator dedicated to high-speed operation. The manipulator system mainly consists of a macro motion stage and a flexible micromanipulator bonded with one macro-fiber-composite actuator. Based on Hamilton's principle and the Bouc-Wen hysteresis equation, the nonlinear dynamic model is obtained. Then, a hybrid control scheme is proposed to simultaneously suppress the elastic vibration during and after the motor motion. In particular, the hybrid control strategy is composed of a trajectory planning approach and an adaptive variable structure control. Moreover, two optimization indices regarding the comprehensive torques and synthesized vibrations are designed, and the optimal trajectories are acquired using a genetic algorithm. Furthermore, a nonlinear fuzzy regulator is used to adjust the switching gain in the variable structure control. Thus, a fuzzy variable structure control with nonlinear adaptive control law is achieved. A series of experiments are performed to verify the effectiveness and feasibility of the established system model and hybrid control strategy. The excited vibration during the motor motion and the residual vibration after the motor motion are decreased. Meanwhile, the settling time is shortened. Both the manipulation stability and operation efficiency of the manipulator are improved by the proposed hybrid strategy.

  17. Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

    NASA Astrophysics Data System (ADS)

    Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

    2009-08-01

    In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

  18. Fuzzy and process modelling of contour ridge water dynamics

    NASA Astrophysics Data System (ADS)

    Mhizha, Alexander; Ndiritu, John

    2018-05-01

    Contour ridges are an in-situ rainwater harvesting technology developed initially for soil erosion control but are currently also widely promoted for rainwater harvesting. The effectiveness of contour ridges depends on geophysical, hydro-climatic and socio economic factors that are highly varied in time and space. Furthermore, field-scale data on these factors are often unavailable. This together with the complexity of hydrological processes at field scale limits the application of classical distributed process modelling to highly-instrumented experimental fields. This paper presents a framework that combines fuzzy logic and process-based approach for modelling contour ridges for rainwater harvesting where detailed field data are not available. Water balance for a representative contour-ridged field incorporating the water flow processes across the boundaries is integrated with fuzzy logic to incorporate the uncertainties in estimating runoff. The model is tested using data collected during the 2009/2010 and 2010/2011 rainfall seasons from two contour-ridged fields in Zhulube located in the semi-arid parts of Zimbabwe. The model is found to replicate soil moisture in the root zone reasonably well (NSE = 0.55 to 0.66 and PBIAS = -1.3 to 6.1 %). The results show that combining fuzzy logic and process based approaches can adequately model soil moisture in a contour ridged-field and could help to assess the water dynamics in contour ridged fields.

  19. Modelling of Reservoir Operations using Fuzzy Logic and ANNs

    NASA Astrophysics Data System (ADS)

    Van De Giesen, N.; Coerver, B.; Rutten, M.

    2015-12-01

    Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, <0,1>, was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.

  20. Fuzzy-neural control of an aircraft tracking camera platform

    NASA Technical Reports Server (NTRS)

    Mcgrath, Dennis

    1994-01-01

    A fuzzy-neural control system simulation was developed for the control of a camera platform used to observe aircraft on final approach to an aircraft carrier. The fuzzy-neural approach to control combines the structure of a fuzzy knowledge base with a supervised neural network's ability to adapt and improve. The performance characteristics of this hybrid system were compared to those of a fuzzy system and a neural network system developed independently to determine if the fusion of these two technologies offers any advantage over the use of one or the other. The results of this study indicate that the fuzzy-neural approach to control offers some advantages over either fuzzy or neural control alone.

  1. A GIS-based fuzzy classification for mapping the agricultural soils for N-fertilizers use.

    PubMed

    Assimakopoulos, J H; Kalivas, D P; Kollias, V J

    2003-06-20

    Special attention should be paid to the choice of the proper N-fertilizer, in order to avoid a further acidification and degradation of acid soils and at the same time to improve nitrogen use efficiency and to limit the nitrate pollution of the ground waters. Therefore, the risk of leaching of the fertilizer and of the acidification of the soils must be considered prior to any N-fertilizer application. The application of N-fertilizers to the soil requires a good knowledge of the soil-fertilizer relationship, which those who are planning the fertilization policy and/or applying it might not have. In this study, a fuzzy classification methodology is presented for mapping the agricultural soils according to the kind and the rate of application of N-fertilizer that should be used. The values of pH, clay, sand and carbonates soil variables are estimated at each point of an area by applying geostatistical techniques. Using the pH values three fuzzy sets: "no-risk-acidification"; "low-risk-acidification"; and "high-risk-acidification" are produced and the memberships of each point to the three sets are estimated. Additionally, from the clay and sand values the membership grade to the fuzzy set "risk-of-leaching" is calculated. The parameters and their values, which are used for the construction of the fuzzy sets, are based on the literature, the existing knowledge and the experimentation, of the soil-fertilizer relationships and provide a consistent mechanism for mapping the soils according to the type of N-fertilizers that should be applied and the rate of applications. The maps produced can easily be interpreted and used by non-experts in the application of the fertilization policy at national, local and farm level. The methodology is presented through a case study using data from the Amfilochia area, west Greece.

  2. Development of Solution Algorithm and Sensitivity Analysis for Random Fuzzy Portfolio Selection Model

    NASA Astrophysics Data System (ADS)

    Hasuike, Takashi; Katagiri, Hideki

    2010-10-01

    This paper focuses on the proposition of a portfolio selection problem considering an investor's subjectivity and the sensitivity analysis for the change of subjectivity. Since this proposed problem is formulated as a random fuzzy programming problem due to both randomness and subjectivity presented by fuzzy numbers, it is not well-defined. Therefore, introducing Sharpe ratio which is one of important performance measures of portfolio models, the main problem is transformed into the standard fuzzy programming problem. Furthermore, using the sensitivity analysis for fuzziness, the analytical optimal portfolio with the sensitivity factor is obtained.

  3. Novel Observer Scheme of Fuzzy-MRAS Sensorless Speed Control of Induction Motor Drive

    NASA Astrophysics Data System (ADS)

    Chekroun, S.; Zerikat, M.; Mechernene, A.; Benharir, N.

    2017-01-01

    This paper presents a novel approach Fuzzy-MRAS conception for robust accurate tracking of induction motor drive operating in a high-performance drives environment. Of the different methods for sensorless control of induction motor drive the model reference adaptive system (MRAS) finds lot of attention due to its good performance. The analysis of the sensorless vector control system using MRAS is presented and the resistance parameters variations and speed observer using new Fuzzy Self-Tuning adaptive IP Controller is proposed. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The present approach helps to achieve a good dynamic response, disturbance rejection and low to plant parameter variations of the induction motor. In order to verify the performances of the proposed observer and control algorithms and to test behaviour of the controlled system, numerical simulation is achieved. Simulation results are presented and discussed to shown the validity and the performance of the proposed observer.

  4. Evolving RBF neural networks for adaptive soft-sensor design.

    PubMed

    Alexandridis, Alex

    2013-12-01

    This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

  5. Fuzzy regression modeling for tool performance prediction and degradation detection.

    PubMed

    Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

    2010-10-01

    In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

  6. Complex fuzzy soft expert sets

    NASA Astrophysics Data System (ADS)

    Selvachandran, Ganeshsree; Hafeed, Nisren A.; Salleh, Abdul Razak

    2017-04-01

    Complex fuzzy sets and its accompanying theory although at its infancy, has proven to be superior to classical type-1 fuzzy sets, due its ability in representing time-periodic problem parameters and capturing the seasonality of the fuzziness that exists in the elements of a set. These are important characteristics that are pervasive in most real world problems. However, there are two major problems that are inherent in complex fuzzy sets: it lacks a sufficient parameterization tool and it does not have a mechanism to validate the values assigned to the membership functions of the elements in a set. To overcome these problems, we propose the notion of complex fuzzy soft expert sets which is a hybrid model of complex fuzzy sets and soft expert sets. This model incorporates the advantages of complex fuzzy sets and soft sets, besides having the added advantage of allowing the users to know the opinion of all the experts in a single model without the need for any additional cumbersome operations. As such, this model effectively improves the accuracy of representation of problem parameters that are periodic in nature, besides having a higher level of computational efficiency compared to similar models in literature.

  7. Systematic methods for the design of a class of fuzzy logic controllers

    NASA Astrophysics Data System (ADS)

    Yasin, Saad Yaser

    2002-09-01

    Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental

  8. Fuzzy time series forecasting model with natural partitioning length approach for predicting the unemployment rate under different degree of confidence

    NASA Astrophysics Data System (ADS)

    Ramli, Nazirah; Mutalib, Siti Musleha Ab; Mohamad, Daud

    2017-08-01

    Fuzzy time series forecasting model has been proposed since 1993 to cater for data in linguistic values. Many improvement and modification have been made to the model such as enhancement on the length of interval and types of fuzzy logical relation. However, most of the improvement models represent the linguistic term in the form of discrete fuzzy sets. In this paper, fuzzy time series model with data in the form of trapezoidal fuzzy numbers and natural partitioning length approach is introduced for predicting the unemployment rate. Two types of fuzzy relations are used in this study which are first order and second order fuzzy relation. This proposed model can produce the forecasted values under different degree of confidence.

  9. Fuzzy control of small servo motors

    NASA Technical Reports Server (NTRS)

    Maor, Ron; Jani, Yashvant

    1993-01-01

    To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.

  10. A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

    NASA Astrophysics Data System (ADS)

    Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

    2018-04-01

    In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

  11. A Hybrid Fuzzy Model for Lean Product Development Performance Measurement

    NASA Astrophysics Data System (ADS)

    Osezua Aikhuele, Daniel; Mohd Turan, Faiz

    2016-02-01

    In the effort for manufacturing companies to meet up with the emerging consumer demands for mass customized products, many are turning to the application of lean in their product development process, and this is gradually moving from being a competitive advantage to a necessity. However, due to lack of clear understanding of the lean performance measurements, many of these companies are unable to implement and fully integrated the lean principle into their product development process. Extensive literature shows that only few studies have focus systematically on the lean product development performance (LPDP) evaluation. In order to fill this gap, the study therefore proposed a novel hybrid model based on Fuzzy Reasoning Approach (FRA), and the extension of Fuzzy-AHP and Fuzzy-TOPSIS methods for the assessment of the LPDP. Unlike the existing methods, the model considers the importance weight of each of the decision makers (Experts) since the performance criteria/attributes are required to be rated, and these experts have different level of expertise. The rating is done using a new fuzzy Likert rating scale (membership-scale) which is designed such that it can address problems resulting from information lost/distortion due to closed-form scaling and the ordinal nature of the existing Likert scale.

  12. Prediction on carbon dioxide emissions based on fuzzy rules

    NASA Astrophysics Data System (ADS)

    Pauzi, Herrini; Abdullah, Lazim

    2014-06-01

    There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

  13. High dimensional model representation method for fuzzy structural dynamics

    NASA Astrophysics Data System (ADS)

    Adhikari, S.; Chowdhury, R.; Friswell, M. I.

    2011-03-01

    Uncertainty propagation in multi-parameter complex structures possess significant computational challenges. This paper investigates the possibility of using the High Dimensional Model Representation (HDMR) approach when uncertain system parameters are modeled using fuzzy variables. In particular, the application of HDMR is proposed for fuzzy finite element analysis of linear dynamical systems. The HDMR expansion is an efficient formulation for high-dimensional mapping in complex systems if the higher order variable correlations are weak, thereby permitting the input-output relationship behavior to be captured by the terms of low-order. The computational effort to determine the expansion functions using the α-cut method scales polynomically with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is first illustrated for multi-parameter nonlinear mathematical test functions with fuzzy variables. The method is then integrated with a commercial finite element software (ADINA). Modal analysis of a simplified aircraft wing with fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations. It is shown that using the proposed HDMR approach, the number of finite element function calls can be reduced without significantly compromising the accuracy.

  14. The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

    PubMed

    Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali

    2016-01-01

    The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

  15. Fast Fuzzy Arithmetic Operations

    NASA Technical Reports Server (NTRS)

    Hampton, Michael; Kosheleva, Olga

    1997-01-01

    In engineering applications of fuzzy logic, the main goal is not to simulate the way the experts really think, but to come up with a good engineering solution that would (ideally) be better than the expert's control, In such applications, it makes perfect sense to restrict ourselves to simplified approximate expressions for membership functions. If we need to perform arithmetic operations with the resulting fuzzy numbers, then we can use simple and fast algorithms that are known for operations with simple membership functions. In other applications, especially the ones that are related to humanities, simulating experts is one of the main goals. In such applications, we must use membership functions that capture every nuance of the expert's opinion; these functions are therefore complicated, and fuzzy arithmetic operations with the corresponding fuzzy numbers become a computational problem. In this paper, we design a new algorithm for performing such operations. This algorithm is applicable in the case when negative logarithms - log(u(x)) of membership functions u(x) are convex, and reduces computation time from O(n(exp 2))to O(n log(n)) (where n is the number of points x at which we know the membership functions u(x)).

  16. Fuzzy bilevel programming with multiple non-cooperative followers: model, algorithm and application

    NASA Astrophysics Data System (ADS)

    Ke, Hua; Huang, Hu; Ralescu, Dan A.; Wang, Lei

    2016-04-01

    In centralized decision problems, it is not complicated for decision-makers to make modelling technique selections under uncertainty. When a decentralized decision problem is considered, however, choosing appropriate models is no longer easy due to the difficulty in estimating the other decision-makers' inconclusive decision criteria. These decision criteria may vary with different decision-makers because of their special risk tolerances and management requirements. Considering the general differences among the decision-makers in decentralized systems, we propose a general framework of fuzzy bilevel programming including hybrid models (integrated with different modelling methods in different levels). Specially, we discuss two of these models which may have wide applications in many fields. Furthermore, we apply the proposed two models to formulate a pricing decision problem in a decentralized supply chain with fuzzy coefficients. In order to solve these models, a hybrid intelligent algorithm integrating fuzzy simulation, neural network and particle swarm optimization based on penalty function approach is designed. Some suggestions on the applications of these models are also presented.

  17. Modelling Of Anticipated Damage Ratio On Breakwaters Using Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Mercan, D. E.; Yagci, O.; Kabdasli, S.

    2003-04-01

    In breakwater design the determination of armour unit weight is especially important in terms of the structure's life. In a typical experimental breakwater stability study, different wave series composed of different wave heights; wave period and wave steepness characteristics are applied in order to investigate performance the structure. Using a classical approach, a regression equation is generated for damage ratio as a function of characteristic wave height. The parameters wave period and wave steepness are not considered. In this study, differing from the classical approach using a fuzzy logic, a relationship between damage ratio as a function of mean wave period (T_m), wave steepness (H_s/L_m) and significant wave height (H_s) was further generated. The system's inputs were mean wave period (T_m), wave steepness (H_s/L_m) and significant wave height (H_s). For fuzzification all input variables were divided into three fuzzy subsets, their membership functions were defined using method developed by Mandani (Mandani, 1974) and the rules were written. While for defuzzification the centroid method was used. In order to calibrate and test the generated models an experimental study was conducted. The experiments were performed in a wave flume (24 m long, 1.0 m wide and 1.0 m high) using 20 different irregular wave series (P-M spectrum). Throughout the study, the water depth was 0.6 m and the breakwater cross-sectional slope was 1V/2H. In the armour layer, a type of artificial armour unit known as antifer cubes were used. The results of the established fuzzy logic model and regression equation model was compared with experimental data and it was determined that the established fuzzy logic model gave a more accurate prediction of the damage ratio on this type of breakwater. References Mandani, E.H., "Application of Fuzzy Algorithms for Control of Simple Dynamic Plant", Proc. IEE, vol. 121, no. 12, December 1974.

  18. Fuzzy logic control of telerobot manipulators

    NASA Technical Reports Server (NTRS)

    Franke, Ernest A.; Nedungadi, Ashok

    1992-01-01

    Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.

  19. A fuzzy set preference model for market share analysis

    NASA Technical Reports Server (NTRS)

    Turksen, I. B.; Willson, Ian A.

    1992-01-01

    Consumer preference models are widely used in new product design, marketing management, pricing, and market segmentation. The success of new products depends on accurate market share prediction and design decisions based on consumer preferences. The vague linguistic nature of consumer preferences and product attributes, combined with the substantial differences between individuals, creates a formidable challenge to marketing models. The most widely used methodology is conjoint analysis. Conjoint models, as currently implemented, represent linguistic preferences as ratio or interval-scaled numbers, use only numeric product attributes, and require aggregation of individuals for estimation purposes. It is not surprising that these models are costly to implement, are inflexible, and have a predictive validity that is not substantially better than chance. This affects the accuracy of market share estimates. A fuzzy set preference model can easily represent linguistic variables either in consumer preferences or product attributes with minimal measurement requirements (ordinal scales), while still estimating overall preferences suitable for market share prediction. This approach results in flexible individual-level conjoint models which can provide more accurate market share estimates from a smaller number of more meaningful consumer ratings. Fuzzy sets can be incorporated within existing preference model structures, such as a linear combination, using the techniques developed for conjoint analysis and market share estimation. The purpose of this article is to develop and fully test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation), and how much to make (market share

  20. A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

    PubMed

    Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias

    2013-10-01

    Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

  1. Modeling Spatial Relationships within a Fuzzy Framework.

    ERIC Educational Resources Information Center

    Petry, Frederick E.; Cobb, Maria A.

    1998-01-01

    Presents a model for representing and storing binary topological and directional relationships between 2-dimensional objects that is used to provide a basis for fuzzy querying capabilities. A data structure called an abstract spatial graph (ASG) is defined for the binary relationships that maintains all necessary information regarding topology and…

  2. A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words

    PubMed Central

    Huang, Yongfeng; Wu, Xian; Li, Xing

    2015-01-01

    With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods. PMID:26106409

  3. Fuzzy regions in an intrinsically disordered protein impair protein-protein interactions.

    PubMed

    Gruet, Antoine; Dosnon, Marion; Blocquel, David; Brunel, Joanna; Gerlier, Denis; Das, Rahul K; Bonetti, Daniela; Gianni, Stefano; Fuxreiter, Monika; Longhi, Sonia; Bignon, Christophe

    2016-02-01

    Despite the partial disorder-to-order transition that intrinsically disordered proteins often undergo upon binding to their partners, a considerable amount of residual disorder may be retained in the bound form, resulting in a fuzzy complex. Fuzzy regions flanking molecular recognition elements may enable partner fishing through non-specific, transient contacts, thereby facilitating binding, but may also disfavor binding through various mechanisms. So far, few computational or experimental studies have addressed the effect of fuzzy appendages on partner recognition by intrinsically disordered proteins. In order to shed light onto this issue, we used the interaction between the intrinsically disordered C-terminal domain of the measles virus (MeV) nucleoprotein (NTAIL ) and the X domain (XD) of the viral phosphoprotein as model system. After binding to XD, the N-terminal region of NTAIL remains conspicuously disordered, with α-helical folding taking place only within a short molecular recognition element. To study the effect of the N-terminal fuzzy region on NTAIL /XD binding, we generated N-terminal truncation variants of NTAIL , and assessed their binding abilities towards XD. The results revealed that binding increases with shortening of the N-terminal fuzzy region, with this also being observed with hsp70 (another MeV NTAIL binding partner), and for the homologous NTAIL /XD pairs from the Nipah and Hendra viruses. Finally, similar results were obtained when the MeV NTAIL fuzzy region was replaced with a highly dissimilar artificial disordered sequence, supporting a sequence-independent inhibitory effect of the fuzzy region. © 2015 Federation of European Biochemical Societies.

  4. Adaptation of influenza A(H1N1)pdm09 virus in experimental mouse models.

    PubMed

    Prokopyeva, E A; Sobolev, I A; Prokopyev, M V; Shestopalov, A M

    2016-04-01

    In the present study, three mouse-adapted variants of influenza A(H1N1)pdm09 virus were obtained by lung-to-lung passages of BALB/c, C57BL/6z and CD1 mice. The significantly increased virulence and pathogenicity of all of the mouse-adapted variants induced 100% mortality in the adapted mice. Genetic analysis indicated that the increased virulence of all of the mouse-adapted variants reflected the incremental acquisition of several mutations in PB2, PB1, HA, NP, NA, and NS2 proteins. Identical amino acid substitutions were also detected in all of the mouse-adapted variants of A(H1N1)pdm09 virus, including PB2 (K251R), PB1 (V652A), NP (I353V), NA (I106V, N248D) and NS1 (G159E). Apparently, influenza A(H1N1)pdm09 virus easily adapted to the host after serial passages in the lungs, inducing 100% lethality in the last experimental group. However, cross-challenge revealed that not all adapted variants are pathogenic for different laboratory mice. Such important results should be considered when using the influenza mice model. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Fuzzy Petri nets to model vision system decisions within a flexible manufacturing system

    NASA Astrophysics Data System (ADS)

    Hanna, Moheb M.; Buck, A. A.; Smith, R.

    1994-10-01

    The paper presents a Petri net approach to modelling, monitoring and control of the behavior of an FMS cell. The FMS cell described comprises a pick and place robot, vision system, CNC-milling machine and 3 conveyors. The work illustrates how the block diagrams in a hierarchical structure can be used to describe events at different levels of abstraction. It focuses on Fuzzy Petri nets (Fuzzy logic with Petri nets) including an artificial neural network (Fuzzy Neural Petri nets) to model and control vision system decisions and robot sequences within an FMS cell. This methodology can be used as a graphical modelling tool to monitor and control the imprecise, vague and uncertain situations, and determine the quality of the output product of an FMS cell.

  6. Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

    PubMed Central

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

  7. Solid oxide fuel cell anode image segmentation based on a novel quantum-inspired fuzzy clustering

    NASA Astrophysics Data System (ADS)

    Fu, Xiaowei; Xiang, Yuhan; Chen, Li; Xu, Xin; Li, Xi

    2015-12-01

    High quality microstructure modeling can optimize the design of fuel cells. For three-phase accurate identification of Solid Oxide Fuel Cell (SOFC) microstructure, this paper proposes a novel image segmentation method on YSZ/Ni anode Optical Microscopic (OM) images. According to Quantum Signal Processing (QSP), the proposed approach exploits a quantum-inspired adaptive fuzziness factor to adaptively estimate the energy function in the fuzzy system based on Markov Random Filed (MRF). Before defuzzification, a quantum-inspired probability distribution based on distance and gray correction is proposed, which can adaptively adjust the inaccurate probability estimation of uncertain points caused by noises and edge points. In this study, the proposed method improves accuracy and effectiveness of three-phase identification on the micro-investigation. It provides firm foundation to investigate the microstructural evolution and its related properties.

  8. Estimation and Modelling of Land Surface Temperature Using Landsat 7 ETM+ Images and Fuzzy System Techniques

    NASA Astrophysics Data System (ADS)

    Bisht, K.; Dodamani, S. S.

    2016-12-01

    Modelling of Land Surface Temperature is essential for short term and long term management of environmental studies and management activities of the Earth's resources. The objective of this research is to estimate and model Land Surface Temperatures (LST). For this purpose, Landsat 7 ETM+ images period from 2007 to 2012 were used for retrieving LST and processed through MATLAB software using Mamdani fuzzy inference systems (MFIS), which includes pre-monsoon and post-monsoon LST in the fuzzy model. The Mangalore City of Karnataka state, India has been taken for this research work. Fuzzy model inputs are considered as the pre-monsoon and post-monsoon retrieved temperatures and LST was chosen as output. In order to develop a fuzzy model for LST, seven fuzzy subsets, nineteen rules and one output are considered for the estimation of weekly mean air temperature. These are very low (VL), low (L), medium low (ML), medium (M), medium high (MH), high (H) and very high (VH). The TVX (Surface Temperature Vegetation Index) and the empirical method have provided estimated LST. The study showed that the Fuzzy model M4/7-19-1 (model 4, 7 fuzzy sets, 19 rules and 1 output) which developed over Mangalore City has provided more accurate outcomes than other models (M1, M2, M3, M5). The result of this research was evaluated according to statistical rules. The best correlation coefficient (R) and root mean squared error (RMSE) between estimated and measured values for pre-monsoon and post-monsoon LST found to be 0.966 - 1.607 K and 0.963- 1.623 respectively.

  9. Fuzzy logic and neural network technologies

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Lea, Robert N.; Savely, Robert T.

    1992-01-01

    Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.

  10. Fuzzy modelling and efficiency in health care systems.

    PubMed

    Ozok, Ahmet F

    2012-01-01

    American Medical Institute reports that each year, because of the medical error, minimum fifty thousand people are dead. For a safety and quality medical system, it is important that information systems are used in health care systems. Health information applications help us to reduce the human error and to support patient care systems. Recently, it is reported that medical information systems applications have also some negative effect on all medical integral elements. The cost of health care information systems is about 4.6% of the total cost. In this paper, it is tried a risk determination model according to principles of fuzzy logic. The improvement of health care systems has become a very popular topic in Turkey recent years. Using necessary information system; it became possible to care patients in a safer way. However, using the necessary HIS tools to manage of administrative and clinical processes at hospitals became more important than before. For example; clinical work flows and communication among pharmacists, nurses and physicians are still not enough investigated. We use fuzzy modeling as a research strategy and developed sum fuzzy membership functions to minimize human error. In application in Turkey the results are significantly related with each other. Besides, the sign differences in health care information systems strongly effects of risk magnitude. The obtained results are discussed and some comments are added.

  11. Inducing Fuzzy Models for Student Classification

    ERIC Educational Resources Information Center

    Nykanen, Ossi

    2006-01-01

    We report an approach for implementing predictive fuzzy systems that manage capturing both the imprecision of the empirically induced classifications and the imprecision of the intuitive linguistic expressions via the extensive use of fuzzy sets. From end-users' point of view, the approach enables encapsulating the technical details of the…

  12. Grey fuzzy optimization model for water quality management of a river system

    NASA Astrophysics Data System (ADS)

    Karmakar, Subhankar; Mujumdar, P. P.

    2006-07-01

    A grey fuzzy optimization model is developed for water quality management of river system to address uncertainty involved in fixing the membership functions for different goals of Pollution Control Agency (PCA) and dischargers. The present model, Grey Fuzzy Waste Load Allocation Model (GFWLAM), has the capability to incorporate the conflicting goals of PCA and dischargers in a deterministic framework. The imprecision associated with specifying the water quality criteria and fractional removal levels are modeled in a fuzzy mathematical framework. To address the imprecision in fixing the lower and upper bounds of membership functions, the membership functions themselves are treated as fuzzy in the model and the membership parameters are expressed as interval grey numbers, a closed and bounded interval with known lower and upper bounds but unknown distribution information. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for different membership functions, specified for different imprecise goals are interval grey numbers in place of a deterministic real number. In the final solution optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. Application of the GFWLAM is illustrated with case study of the Tunga-Bhadra river system in India.

  13. Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

    NASA Astrophysics Data System (ADS)

    Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

    2013-04-01

    SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

  14. Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management.

    PubMed

    Li, Yongping; Huang, Guo H; Veawab, Amornvadee; Nie, Xianghui; Liu, Lei

    2006-08-01

    In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.

  15. A Comparison of Fuzzy Models in Similarity Assessment of Misregistered Area Class Maps

    NASA Astrophysics Data System (ADS)

    Brown, Scott

    Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity.

  16. Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method

    NASA Astrophysics Data System (ADS)

    Mamonova, T.; Syryamkin, V.; Vasilyeva, T.

    2016-04-01

    The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.

  17. A fuzzy-theory-based behavioral model for studying pedestrian evacuation from a single-exit room

    NASA Astrophysics Data System (ADS)

    Fu, Libi; Song, Weiguo; Lo, Siuming

    2016-08-01

    Many mass events in recent years have highlighted the importance of research on pedestrian evacuation dynamics. A number of models have been developed to analyze crowd behavior under evacuation situations. However, few focus on pedestrians' decision-making with respect to uncertainty, vagueness and imprecision. In this paper, a discrete evacuation model defined on the cellular space is proposed according to the fuzzy theory which is able to describe imprecise and subjective information. Pedestrians' percept information and various characteristics are regarded as fuzzy input. Then fuzzy inference systems with rule bases, which resemble human reasoning, are established to obtain fuzzy output that decides pedestrians' movement direction. This model is tested in two scenarios, namely in a single-exit room with and without obstacles. Simulation results reproduce some classic dynamics phenomena discovered in real building evacuation situations, and are consistent with those in other models and experiments. It is hoped that this study will enrich movement rules and approaches in traditional cellular automaton models for evacuation dynamics.

  18. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

    NASA Astrophysics Data System (ADS)

    Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

    2017-11-01

    The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

  19. Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis

    NASA Astrophysics Data System (ADS)

    Fu, Pei-hua; Yin, Hong-bo

    In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.

  20. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

    NASA Astrophysics Data System (ADS)

    Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

    2016-09-01

    This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

  1. Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

    PubMed Central

    Sun, Baoliang; Jiang, Chunlan; Li, Ming

    2016-01-01

    An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

  2. Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

    NASA Astrophysics Data System (ADS)

    Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

    2012-04-01

    Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

  3. Definition of zones with different levels of productivity within an agricultural field using fuzzy modeling

    USDA-ARS?s Scientific Manuscript database

    Zoning of agricultural fields is an important task for utilization of precision farming technology. One method for the definition of zones with different levels of productivity is based on fuzzy indicator model. Fuzzy indicator model for identification of zones with different levels of productivit...

  4. A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

    NASA Astrophysics Data System (ADS)

    Tian, Wenli; Cao, Chengxuan

    2017-03-01

    A generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.

  5. Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration

    NASA Astrophysics Data System (ADS)

    Shiri, Jalal; Nazemi, Amir Hossein; Sadraddini, Ali Ashraf; Landeras, Gorka; Kisi, Ozgur; Fard, Ahmad Fakheri; Marti, Pau

    2013-02-01

    SummaryAccurate estimation of reference evapotranspiration is important for irrigation scheduling, water resources management and planning and other agricultural water management issues. In the present paper, the capabilities of generalized neuro-fuzzy models were evaluated for estimating reference evapotranspiration using two separate sets of weather data from humid and non-humid regions of Spain and Iran. In this way, the data from some weather stations in the Basque Country and Valencia region (Spain) were used for training the neuro-fuzzy models [in humid and non-humid regions, respectively] and subsequently, the data from these regions were pooled to evaluate the generalization capability of a general neuro-fuzzy model in humid and non-humid regions. The developed models were tested in stations of Iran, located in humid and non-humid regions. The obtained results showed the capabilities of generalized neuro-fuzzy model in estimating reference evapotranspiration in different climatic zones. Global GNF models calibrated using both non-humid and humid data were found to successfully estimate ET0 in both non-humid and humid regions of Iran (the lowest MAE values are about 0.23 mm for non-humid Iranian regions and 0.12 mm for humid regions). non-humid GNF models calibrated using non-humid data performed much better than the humid GNF models calibrated using humid data in non-humid region while the humid GNF model gave better estimates in humid region.

  6. Usage Intention Framework Model: A Fuzzy Logic Interpretation of the Classical Utaut Model

    ERIC Educational Resources Information Center

    Sandaire, Johnny

    2009-01-01

    A fuzzy conjoint analysis (FCA: Turksen, 1992) model for enhancing management decision in the technology adoption domain was implemented as an extension to the UTAUT model (Venkatesh, Morris, Davis, & Davis, 2003). Additionally, a UTAUT-based Usage Intention Framework Model (UIFM) introduced a closed-loop feedback system. The empirical evidence…

  7. Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Brandon, Jay M.; Morelli, Eugene A.

    2012-01-01

    Results of the Aeronautics Research Mission Directorate Seedling Project Phase I research project entitled "Nonlinear Aerodynamics Modeling using Fuzzy Logic" are presented. Efficient and rapid flight test capabilities were developed for estimating highly nonlinear models of airplane aerodynamics over a large flight envelope. Results showed that the flight maneuvers developed, used in conjunction with the fuzzy-logic system identification algorithms, produced very good model fits of the data, with no model structure inputs required, for flight conditions ranging from cruise to departure and spin conditions.

  8. Learning fuzzy information in a hybrid connectionist, symbolic model

    NASA Technical Reports Server (NTRS)

    Romaniuk, Steve G.; Hall, Lawrence O.

    1993-01-01

    An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

  9. Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.

    PubMed

    Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

    2015-08-18

    Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.

  10. Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

    NASA Astrophysics Data System (ADS)

    Wang, Wei-Cheng; Tai, Cheng-Chi

    2017-07-01

    The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

  11. TOPSIS-based consensus model for group decision-making with incomplete interval fuzzy preference relations.

    PubMed

    Liu, Fang; Zhang, Wei-Guo

    2014-08-01

    Due to the vagueness of real-world environments and the subjective nature of human judgments, it is natural for experts to estimate their judgements by using incomplete interval fuzzy preference relations. In this paper, based on the technique for order preference by similarity to ideal solution method, we present a consensus model for group decision-making (GDM) with incomplete interval fuzzy preference relations. To do this, we first define a new consistency measure for incomplete interval fuzzy preference relations. Second, a goal programming model is proposed to estimate the missing interval preference values and it is guided by the consistency property. Third, an ideal interval fuzzy preference relation is constructed by using the induced ordered weighted averaging operator, where the associated weights of characterizing the operator are based on the defined consistency measure. Fourth, a similarity degree between complete interval fuzzy preference relations and the ideal one is defined. The similarity degree is related to the associated weights, and used to aggregate the experts' preference relations in such a way that more importance is given to ones with the higher similarity degree. Finally, a new algorithm is given to solve the GDM problem with incomplete interval fuzzy preference relations, which is further applied to partnership selection in formation of virtual enterprises.

  12. Genetic Algorithm for Solving Fuzzy Shortest Path Problem in a Network with mixed fuzzy arc lengths

    NASA Astrophysics Data System (ADS)

    Mahdavi, Iraj; Tajdin, Ali; Hassanzadeh, Reza; Mahdavi-Amiri, Nezam; Shafieian, Hosna

    2011-06-01

    We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model.

  13. Modified Levenberg-Marquardt Method for RÖSSLER Chaotic System Fuzzy Modeling Training

    NASA Astrophysics Data System (ADS)

    Wang, Yu-Hui; Wu, Qing-Xian; Jiang, Chang-Sheng; Xue, Ya-Li; Fang, Wei

    Generally, fuzzy approximation models require some human knowledge and experience. Operator's experience is involved in the mathematics of fuzzy theory as a collection of heuristic rules. The main goal of this paper is to present a new method for identifying unknown nonlinear dynamics such as Rössler system without any human knowledge. Instead of heuristic rules, the presented method uses the input-output data pairs to identify the Rössler chaotic system. The training algorithm is a modified Levenberg-Marquardt (L-M) method, which can adjust the parameters of each linear polynomial and fuzzy membership functions on line, and do not rely on experts' experience excessively. Finally, it is applied to training Rössler chaotic system fuzzy identification. Comparing this method with the standard L-M method, the convergence speed is accelerated. The simulation results demonstrate the effectiveness of the proposed method.

  14. North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 1

    NASA Technical Reports Server (NTRS)

    Villarreal, James A. (Compiler)

    1992-01-01

    This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such as neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

  15. North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

    NASA Technical Reports Server (NTRS)

    Villarreal, James A. (Compiler)

    1992-01-01

    This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

  16. Professional Learning: A Fuzzy Logic-Based Modelling Approach

    ERIC Educational Resources Information Center

    Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.

    2007-01-01

    Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…

  17. Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

    NASA Astrophysics Data System (ADS)

    Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

    2016-05-01

    Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

  18. Fuzzy connectedness and object definition

    NASA Astrophysics Data System (ADS)

    Udupa, Jayaram K.; Samarasekera, Supun

    1995-04-01

    Approaches to object information extraction from images should attempt to use the fact that images are fuzzy. In past image segmentation research, the notion of `hanging togetherness' of image elements specified by their fuzzy connectedness has been lacking. We present a theory of fuzzy objects for n-dimensional digital spaces based on a notion of fuzzy connectedness of image elements. Although our definitions lead to problems of enormous combinatorial complexity, the theoretical results allow us to reduce this dramatically. We demonstrate the utility of the theory and algorithms in image segmentation based on several practical examples.

  19. A fuzzy case based reasoning tool for model based approach to rocket engine health monitoring

    NASA Technical Reports Server (NTRS)

    Krovvidy, Srinivas; Nolan, Adam; Hu, Yong-Lin; Wee, William G.

    1992-01-01

    In this system we develop a fuzzy case based reasoner that can build a case representation for several past anomalies detected, and we develop case retrieval methods that can be used to index a relevant case when a new problem (case) is presented using fuzzy sets. The choice of fuzzy sets is justified by the uncertain data. The new problem can be solved using knowledge of the model along with the old cases. This system can then be used to generalize the knowledge from previous cases and use this generalization to refine the existing model definition. This in turn can help to detect failures using the model based algorithms.

  20. Intelligent Paging Based Mobile User Tracking Using Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Saha, Sajal; Dutta, Raju; Debnath, Soumen; Mukhopadhyay, Asish K.

    2010-11-01

    In general, a mobile user travels in a predefined path that depends mostly on the user's characteristics. Thus, tracking the locations of a mobile user is one of the challenges for location management. In this paper, we introduce a movement pattern learning strategy system to track the user's movements using adaptive fuzzy logic. Our fuzzy inference system extracts patterns from the historical data record of the cell numbers along with the date and time stamp of the users occupying the cell. Implementation of this strategy has been evaluated with the real time user data which proves the efficiency and accuracy of the model. This mechanism not only reduces user location tracking costs, but also significantly decreases the call-loss rates and average paging delays.

  1. Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

    NASA Astrophysics Data System (ADS)

    Verma, Akhilesh K.; Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.; Jenamani, Mamata

    2014-12-01

    In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root

  2. LMI-Based Fuzzy Optimal Variance Control of Airfoil Model Subject to Input Constraints

    NASA Technical Reports Server (NTRS)

    Swei, Sean S.M.; Ayoubi, Mohammad A.

    2017-01-01

    This paper presents a study of fuzzy optimal variance control problem for dynamical systems subject to actuator amplitude and rate constraints. Using Takagi-Sugeno fuzzy modeling and dynamic Parallel Distributed Compensation technique, the stability and the constraints can be cast as a multi-objective optimization problem in the form of Linear Matrix Inequalities. By utilizing the formulations and solutions for the input and output variance constraint problems, we develop a fuzzy full-state feedback controller. The stability and performance of the proposed controller is demonstrated through its application to the airfoil flutter suppression.

  3. Use of fuzzy sets in modeling of GIS objects

    NASA Astrophysics Data System (ADS)

    Mironova, Yu N.

    2018-05-01

    The paper discusses modeling and methods of data visualization in geographic information systems. Information processing in Geoinformatics is based on the use of models. Therefore, geoinformation modeling is a key in the chain of GEODATA processing. When solving problems, using geographic information systems often requires submission of the approximate or insufficient reliable information about the map features in the GIS database. Heterogeneous data of different origin and accuracy have some degree of uncertainty. In addition, not all information is accurate: already during the initial measurements, poorly defined terms and attributes (e.g., "soil, well-drained") are used. Therefore, there are necessary methods for working with uncertain requirements, classes, boundaries. The author proposes using spatial information fuzzy sets. In terms of a characteristic function, a fuzzy set is a natural generalization of ordinary sets, when one rejects the binary nature of this feature and assumes that it can take any value in the interval.

  4. A fuzzy call admission control scheme in wireless networks

    NASA Astrophysics Data System (ADS)

    Ma, Yufeng; Gong, Shenguang; Hu, Xiulin; Zhang, Yunyu

    2007-11-01

    Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy scheme with an adaptive channel reservation scheme. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

  5. A neuro-fuzzy architecture for real-time applications

    NASA Technical Reports Server (NTRS)

    Ramamoorthy, P. A.; Huang, Song

    1992-01-01

    Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

  6. The architecture of adaptive neural network based on a fuzzy inference system for implementing intelligent control in photovoltaic systems

    NASA Astrophysics Data System (ADS)

    Gimazov, R.; Shidlovskiy, S.

    2018-05-01

    In this paper, we consider the architecture of the algorithm for extreme regulation in the photovoltaic system. An algorithm based on an adaptive neural network with fuzzy inference is proposed. The implementation of such an algorithm not only allows solving a number of problems in existing algorithms for extreme power regulation of photovoltaic systems, but also creates a reserve for the creation of a universal control system for a photovoltaic system.

  7. A hybrid skull-stripping algorithm based on adaptive balloon snake models

    NASA Astrophysics Data System (ADS)

    Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua

    2013-02-01

    Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.

  8. Streamflow Forecasting Using Nuero-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Nanduri, U. V.; Swain, P. C.

    2005-12-01

    The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

  9. Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

    PubMed

    Kamesh, Reddi; Rani, K Yamuna

    2016-09-01

    A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Fuzzy Modelling for Human Dynamics Based on Online Social Networks

    PubMed Central

    Cuenca-Jara, Jesus; Valdes-Vela, Mercedes; Skarmeta, Antonio F.

    2017-01-01

    Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. PMID:28837120

  11. Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

    PubMed

    Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

    2017-08-24

    Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

  12. Forecasting Enrollments with Fuzzy Time Series.

    ERIC Educational Resources Information Center

    Song, Qiang; Chissom, Brad S.

    The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

  13. A Fuzzy mathematical model to estimate the effects of global warming on the vitality of Laelia purpurata orchids.

    PubMed

    Putti, Fernando Ferrari; Filho, Luis Roberto Almeida Gabriel; Gabriel, Camila Pires Cremasco; Neto, Alfredo Bonini; Bonini, Carolina Dos Santos Batista; Rodrigues Dos Reis, André

    2017-06-01

    This study aimed to develop a fuzzy mathematical model to estimate the impacts of global warming on the vitality of Laelia purpurata growing in different Brazilian environmental conditions. In order to develop the mathematical model was considered as intrinsic factors the parameters: temperature, humidity and shade conditions to determine the vitality of plants. Fuzzy model results could accurately predict the optimal conditions for cultivation of Laelia purpurata in several sites of Brazil. Based on fuzzy model results, we found that higher temperatures and lacking of properly shading can reduce the vitality of orchids. Fuzzy mathematical model could precisely detect the effect of higher temperatures causing damages on vitality of plants as a consequence of global warming. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Using Evolved Fuzzy Neural Networks for Injury Detection from Isokinetic Curves

    NASA Astrophysics Data System (ADS)

    Couchet, Jorge; Font, José María; Manrique, Daniel

    In this paper we propose an evolutionary fuzzy neural networks system for extracting knowledge from a set of time series containing medical information. The series represent isokinetic curves obtained from a group of patients exercising the knee joint on an isokinetic dynamometer. The system has two parts: i) it analyses the time series input in order generate a simplified model of an isokinetic curve; ii) it applies a grammar-guided genetic program to obtain a knowledge base represented by a fuzzy neural network. Once the knowledge base has been generated, the system is able to perform knee injuries detection. The results suggest that evolved fuzzy neural networks perform better than non-evolutionary approaches and have a high accuracy rate during both the training and testing phases. Additionally, they are robust, as the system is able to self-adapt to changes in the problem without human intervention.

  15. RETRACTED: Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system

    NASA Astrophysics Data System (ADS)

    Petković, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlović, Nenad T.; Akib, Shatirah

    2014-07-01

    This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor. Sections ;1. Introduction; and ;2. Modulation transfer function;, as well as Figures 1-3, plagiarize the article published by N. Gül and M. Efe in Turk J Elec Eng & Comp Sci 18 (2010) 71 (http://journals.tubitak.gov.tr/elektrik/issues/elk-10-18-1/elk-18-1-6-0811-9.pdf). Sections ;4. Adaptive neuro-fuzzy inference system; and ;6. Conclusion; duplicate parts of the articles previously published by the corresponding author et al in ;Expert Systems with Applications; 39 (2012) 13295-13304, http://dx.doi.org/10.1016/j.eswa.2012.05.072 and ;Expert Systems with Applications; 40 (2013) 281-286, http://dx.doi.org/10.1016/j.eswa.2012.07.076. One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper is not under consideration for publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents an abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

  16. Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.

    PubMed

    Bratsas, Charalampos; Koutkias, Vassilis; Kaimakamis, Evangelos; Bamidis, Panagiotis; Maglaveras, Nicos

    2007-01-01

    Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

  17. Design of a modified adaptive neuro fuzzy inference system classifier for medical diagnosis of Pima Indians Diabetes

    NASA Astrophysics Data System (ADS)

    Sagir, Abdu Masanawa; Sathasivam, Saratha

    2017-08-01

    Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most of the diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system with Modified Levenberg-Marquardt algorithm using analytical derivation scheme for computation of Jacobian matrix. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about presence or absence of a disease. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent system was tested with Pima Indian Diabetes dataset obtained from the University of California at Irvine's (UCI) machine learning repository. The proposed method's performance was evaluated based on training and test datasets. In addition, an attempt was done to specify the effectiveness of the performance measuring total accuracy, sensitivity and specificity. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.

  18. Fuzzy pharmacology: theory and applications.

    PubMed

    Sproule, Beth A; Naranjo, Claudio A; Türksen, I Burhan

    2002-09-01

    Fuzzy pharmacology is a term coined to represent the application of fuzzy logic and fuzzy set theory to pharmacological problems. Fuzzy logic is the science of reasoning, thinking and inference that recognizes and uses the real world phenomenon that everything is a matter of degree. It is an extension of binary logic that is able to deal with complex systems because it does not require crisp definitions and distinctions for the system components. In pharmacology, fuzzy modeling has been used for the mechanical control of drug delivery in surgical settings, and work has begun evaluating its use in other pharmacokinetic and pharmacodynamic applications. Fuzzy pharmacology is an emerging field that, based on these initial explorations, warrants further investigation.

  19. Software for occupational health and safety risk analysis based on a fuzzy model.

    PubMed

    Stefanovic, Miladin; Tadic, Danijela; Djapan, Marko; Macuzic, Ivan

    2012-01-01

    Risk and safety management are very important issues in healthcare systems. Those are complex systems with many entities, hazards and uncertainties. In such an environment, it is very hard to introduce a system for evaluating and simulating significant hazards. In this paper, we analyzed different types of hazards in healthcare systems and we introduced a new fuzzy model for evaluating and ranking hazards. Finally, we presented a developed software solution, based on the suggested fuzzy model for evaluating and monitoring risk.

  20. Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

    PubMed

    Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

    2017-12-01

    Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

  1. Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method

    PubMed Central

    Alguliyev, Rasim M.; Aliguliyev, Ramiz M.; Mahmudova, Rasmiyya S.

    2015-01-01

    Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method. PMID:26516634

  2. An Intuitionistic Fuzzy Logic Models for Multicriteria Decision Making Under Uncertainty

    NASA Astrophysics Data System (ADS)

    Jana, Biswajit; Mohanty, Sachi Nandan

    2017-04-01

    The purpose of this paper is to enhance the applicability of the fuzzy sets for developing mathematical models for decision making under uncertainty, In general a decision making process consist of four stages, namely collection of information from various sources, compile the information, execute the information and finally take the decision/action. Only fuzzy sets theory is capable to quantifying the linguistic expression to mathematical form in complex situation. Intuitionistic fuzzy set (IFSs) which reflects the fact that the degree of non membership is not always equal to one minus degree of membership. There may be some degree of hesitation. Thus, there are some situations where IFS theory provides a more meaningful and applicable to cope with imprecise information present for solving multiple criteria decision making problem. This paper emphasis on IFSs, which is help for solving real world problem in uncertainty situation.

  3. Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks

    PubMed Central

    Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

    2015-01-01

    Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238

  4. Modelling Multi Hazard Mapping in Semarang City Using GIS-Fuzzy Method

    NASA Astrophysics Data System (ADS)

    Nugraha, A. L.; Awaluddin, M.; Sasmito, B.

    2018-02-01

    One important aspect of disaster mitigation planning is hazard mapping. Hazard mapping can provide spatial information on the distribution of locations that are threatened by disaster. Semarang City as the capital of Central Java Province is one of the cities with high natural disaster intensity. Frequent natural disasters Semarang city is tidal flood, floods, landslides, and droughts. Therefore, Semarang City needs spatial information by doing multi hazard mapping to support disaster mitigation planning in Semarang City. Multi Hazards map modelling can be derived from parameters such as slope maps, rainfall, land use, and soil types. This modelling is done by using GIS method with scoring and overlay technique. However, the accuracy of modelling would be better if the GIS method is combined with Fuzzy Logic techniques to provide a good classification in determining disaster threats. The Fuzzy-GIS method will build a multi hazards map of Semarang city can deliver results with good accuracy and with appropriate threat class spread so as to provide disaster information for disaster mitigation planning of Semarang city. from the multi-hazard modelling using GIS-Fuzzy can be known type of membership that has a good accuracy is the type of membership Gauss with RMSE of 0.404 the smallest of the other membership and VAF value of 72.909% of the largest of the other membership.

  5. Fuzzy and neural control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  6. Application of Fuzzy Reasoning for Filtering and Enhancement of Ultrasonic Images

    NASA Technical Reports Server (NTRS)

    Sacha, J. P.; Cios, K. J.; Roth, D. J.; Berke, L.; Vary, A.

    1994-01-01

    This paper presents a new type of an adaptive fuzzy operator for detection of isolated abnormalities, and enhancement of raw ultrasonic images. Fuzzy sets used in decision rules are defined for each image based on empirical statistics of the color intensities. Examples of the method are also presented in the paper.

  7. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    NASA Technical Reports Server (NTRS)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  8. Collaborative filtering recommendation model based on fuzzy clustering algorithm

    NASA Astrophysics Data System (ADS)

    Yang, Ye; Zhang, Yunhua

    2018-05-01

    As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

  9. Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques.

    PubMed

    Navas, Juan Moreno; Telfer, Trevor C; Ross, Lindsay G

    2011-08-01

    Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development. The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation. Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

    PubMed

    Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

    2007-10-01

    Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

  11. Prediction of soft soil foundation settlement in Guangxi granite area based on fuzzy neural network model

    NASA Astrophysics Data System (ADS)

    Luo, Junhui; Wu, Chao; Liu, Xianlin; Mi, Decai; Zeng, Fuquan; Zeng, Yongjun

    2018-01-01

    At present, the prediction of soft foundation settlement mostly use the exponential curve and hyperbola deferred approximation method, and the correlation between the results is poor. However, the application of neural network in this area has some limitations, and none of the models used in the existing cases adopted the TS fuzzy neural network of which calculation combines the characteristics of fuzzy system and neural network to realize the mutual compatibility methods. At the same time, the developed and optimized calculation program is convenient for engineering designers. Taking the prediction and analysis of soft foundation settlement of gully soft soil in granite area of Guangxi Guihe road as an example, the fuzzy neural network model is established and verified to explore the applicability. The TS fuzzy neural network is used to construct the prediction model of settlement and deformation, and the corresponding time response function is established to calculate and analyze the settlement of soft foundation. The results show that the prediction of short-term settlement of the model is accurate and the final settlement prediction result has certain engineering reference value.

  12. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    NASA Astrophysics Data System (ADS)

    Ajay Kumar, M.; Srikanth, N. V.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

  13. Fuzzy inference game approach to uncertainty in business decisions and market competitions.

    PubMed

    Oderanti, Festus Oluseyi

    2013-01-01

    The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

  14. Intelligent fuzzy controller for event-driven real time systems

    NASA Technical Reports Server (NTRS)

    Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

    1992-01-01

    Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

  15. Multilayer perceptron, fuzzy sets, and classification

    NASA Technical Reports Server (NTRS)

    Pal, Sankar K.; Mitra, Sushmita

    1992-01-01

    A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

  16. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Xu, Rui; Zhou, Miaolei

    2018-04-01

    Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

  17. Neural networks with fuzzy Petri nets for modeling a machining process

    NASA Astrophysics Data System (ADS)

    Hanna, Moheb M.

    1998-03-01

    The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

  18. Forecasting of natural gas consumption with neural network and neuro fuzzy system

    NASA Astrophysics Data System (ADS)

    Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan

    2010-05-01

    The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting

  19. Fuzzy logic-based flight control system design

    NASA Astrophysics Data System (ADS)

    Nho, Kyungmoon

    The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.

  20. PI and fuzzy logic controllers for shunt Active Power Filter--a report.

    PubMed

    P, Karuppanan; Mahapatra, Kamala Kanta

    2012-01-01

    This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  1. A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

    NASA Astrophysics Data System (ADS)

    Ashrafi, Mohammad; Chua, Lloyd Hock Chye; Quek, Chai; Qin, Xiaosheng

    2017-02-01

    Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to

  2. Inexact nonlinear improved fuzzy chance-constrained programming model for irrigation water management under uncertainty

    NASA Astrophysics Data System (ADS)

    Zhang, Chenglong; Zhang, Fan; Guo, Shanshan; Liu, Xiao; Guo, Ping

    2018-01-01

    An inexact nonlinear mλ-measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), mλ-measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas.

  3. Train repathing in emergencies based on fuzzy linear programming.

    PubMed

    Meng, Xuelei; Cui, Bingmou

    2014-01-01

    Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.

  4. A Fuzzy Query Mechanism for Human Resource Websites

    NASA Astrophysics Data System (ADS)

    Lai, Lien-Fu; Wu, Chao-Chin; Huang, Liang-Tsung; Kuo, Jung-Chih

    Users' preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. User's fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to user's preference.

  5. A new fuzzy sliding mode controller for vibration control systems using integrated-structure smart dampers

    NASA Astrophysics Data System (ADS)

    Dzung Nguyen, Sy; Kim, Wanho; Park, Jhinha; Choi, Seung-Bok

    2017-04-01

    Vibration control systems using smart dampers (SmDs) such as magnetorheological and electrorheological dampers (MRD and ERD), which are classified as the integrated structure-SmD control systems (ISSmDCSs), have been actively researched and widely used. This work proposes a new controller for a class of ISSmDCSs in which high accuracy of SmD models as well as increment of control ability to deal with uncertainty and time delay are to be expected. In order to achieve this goal, two formualtion steps are required; a non-parametric SmD model based on an adaptive neuro-fuzzy inference system (ANFIS) and a novel fuzzy sliding mode controller (FSMC) which can weaken the model error of the ISSmDCSs and hence provide enhanced vibration control performances. As for the formulation of the proposed controller, first, an ANFIS controller is desgned to identify SmDs using the improved control algorithm named improved establishing neuro-fuzzy system (establishing neuro-fuzzy system). Second, a new control law for the FSMC is designed via Lyapunov stability analysis. An application to a semi-active MRD vehicle suspension system is then undertaken to illustrate and evaluate the effectiveness of the proposed control method. It is demonstrated through an experimental realization that the FSMC proposed in this work shows superior vibration control performance of the vehicle suspension compared to other surveyed controller which have similar structures to the FSMC, such as fuzzy logic and sliding mode control.

  6. Fuzzy Neural Networks for Decision Support in Negotiation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

    There is a large number of parameters which one can take into account when building a negotiation model. These parameters in general are uncertain, thus leading to models which represents them with fuzzy sets. On the other hand, the nature of these parameters makes them very difficult to model them with precise values. During negotiation, these parameters play an important role by altering the outcomes or changing the state of the negotiators. One reasonable way to model this procedure is to accept fuzzy relations (from theory or experience). The action of these relations to fuzzy sets, produce new fuzzy setsmore » which describe now the new state of the system or the modified parameters. But, in the majority of these situations, the relations are multidimensional, leading to complicated models and exponentially increasing computational time. In this paper a solution to this problem is presented. The use of fuzzy neural networks is shown that it can substitute the use of fuzzy relations with comparable results. Finally a simple simulation is carried in order to test the new method.« less

  7. Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon

    NASA Astrophysics Data System (ADS)

    Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.

    2014-10-01

    In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (<22.46 Å) and average particle size lower than 48.8 Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.

  8. Explorations in fuzzy physics and non-commutative geometry

    NASA Astrophysics Data System (ADS)

    Kurkcuoglu, Seckin

    Fuzzy spaces arise as discrete approximations to continuum manifolds. They are usually obtained through quantizing coadjoint orbits of compact Lie groups and they can be described in terms of finite-dimensional matrix algebras, which for large matrix sizes approximate the algebra of functions of the limiting continuum manifold. Their ability to exactly preserve the symmetries of their parent manifolds is especially appealing for physical applications. Quantum Field Theories are built over them as finite-dimensional matrix models preserving almost all the symmetries of their respective continuum models. In this dissertation, we first focus our attention to the study of fuzzy supersymmetric spaces. In this regard, we obtain the fuzzy supersphere S2,2F through quantizing the supersphere, and demonstrate that it has exact supersymmetry. We derive a finite series formula for the *-product of functions over S2,2F and analyze the differential geometric information encoded in this formula. Subsequently, we show that quantum field theories on S2,2F are realized as finite-dimensional supermatrix models, and in particular we obtain the non-linear sigma model over the fuzzy supersphere by constructing the fuzzy supersymmetric extensions of a certain class of projectors. We show that this model too, is realized as a finite-dimensional supermatrix model with exact supersymmetry. Next, we show that fuzzy spaces have a generalized Hopf algebra structure. By focusing on the fuzzy sphere, we establish that there is a *-homomorphism from the group algebra SU(2)* of SU(2) to the fuzzy sphere. Using this and the canonical Hopf algebra structure of SU(2)* we show that both the fuzzy sphere and their direct sum are Hopf algebras. Using these results, we discuss processes in which a fuzzy sphere with angular momenta J splits into fuzzy spheres with angular momenta K and L. Finally, we study the formulation of Chern-Simons (CS) theory on an infinite strip of the non-commutative plane. We

  9. Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images.

    PubMed

    Udupa, Jayaram K; Odhner, Dewey; Zhao, Liming; Tong, Yubing; Matsumoto, Monica M S; Ciesielski, Krzysztof C; Falcao, Alexandre X; Vaideeswaran, Pavithra; Ciesielski, Victoria; Saboury, Babak; Mohammadianrasanani, Syedmehrdad; Sin, Sanghun; Arens, Raanan; Torigian, Drew A

    2014-07-01

    To make Quantitative Radiology (QR) a reality in radiological practice, computerized body-wide Automatic Anatomy Recognition (AAR) becomes essential. With the goal of building a general AAR system that is not tied to any specific organ system, body region, or image modality, this paper presents an AAR methodology for localizing and delineating all major organs in different body regions based on fuzzy modeling ideas and a tight integration of fuzzy models with an Iterative Relative Fuzzy Connectedness (IRFC) delineation algorithm. The methodology consists of five main steps: (a) gathering image data for both building models and testing the AAR algorithms from patient image sets existing in our health system; (b) formulating precise definitions of each body region and organ and delineating them following these definitions; (c) building hierarchical fuzzy anatomy models of organs for each body region; (d) recognizing and locating organs in given images by employing the hierarchical models; and (e) delineating the organs following the hierarchy. In Step (c), we explicitly encode object size and positional relationships into the hierarchy and subsequently exploit this information in object recognition in Step (d) and delineation in Step (e). Modality-independent and dependent aspects are carefully separated in model encoding. At the model building stage, a learning process is carried out for rehearsing an optimal threshold-based object recognition method. The recognition process in Step (d) starts from large, well-defined objects and proceeds down the hierarchy in a global to local manner. A fuzzy model-based version of the IRFC algorithm is created by naturally integrating the fuzzy model constraints into the delineation algorithm. The AAR system is tested on three body regions - thorax (on CT), abdomen (on CT and MRI), and neck (on MRI and CT) - involving a total of over 35 organs and 130 data sets (the total used for model building and testing). The training and

  10. Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

    NASA Astrophysics Data System (ADS)

    Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh

    2017-07-01

    In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

  11. Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.

    PubMed

    Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O

    2012-06-01

    It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. Modeling uncertainty in computerized guidelines using fuzzy logic.

    PubMed Central

    Jaulent, M. C.; Joyaux, C.; Colombet, I.; Gillois, P.; Degoulet, P.; Chatellier, G.

    2001-01-01

    Computerized Clinical Practice Guidelines (CPGs) improve quality of care by assisting physicians in their decision making. A number of problems emerges since patients with close characteristics are given contradictory recommendations. In this article, we propose to use fuzzy logic to model uncertainty due to the use of thresholds in CPGs. A fuzzy classification procedure has been developed that provides for each message of the CPG, a strength of recommendation that rates the appropriateness of the recommendation for the patient under consideration. This work is done in the context of a CPG for the diagnosis and the management of hypertension, published in 1997 by the French agency ANAES. A population of 82 patients with mild to moderate hypertension was selected and the results of the classification system were compared to whose given by a classical decision tree. Observed agreement is 86.6% and the variability of recommendations for patients with close characteristics is reduced. PMID:11825196

  13. A Fuzzy-Based Decision Support Model for Selecting the Best Dialyser Flux in Haemodialysis.

    PubMed

    Oztürk, Necla; Tozan, Hakan

    2015-01-01

    Decision making is an important procedure for every organization. The procedure is particularly challenging for complicated multi-criteria problems. Selection of dialyser flux is one of the decisions routinely made for haemodialysis treatment provided for chronic kidney failure patients. This study provides a decision support model for selecting the best dialyser flux between high-flux and low-flux dialyser alternatives. The preferences of decision makers were collected via a questionnaire. A total of 45 questionnaires filled by dialysis physicians and nephrologists were assessed. A hybrid fuzzy-based decision support software that enables the use of Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Analytic Network Process (ANP), and Fuzzy Analytic Network Process (FANP) was used to evaluate the flux selection model. In conclusion, the results showed that a high-flux dialyser is the best. option for haemodialysis treatment.

  14. Fuzzy jets

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variablesmore » in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less

  15. Fuzzy jets

    DOE PAGES

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; ...

    2016-06-01

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variablesmore » in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less

  16. Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process

    NASA Astrophysics Data System (ADS)

    Sivaraos; Khalim, A. Z.; Salleh, M. S.; Sivakumar, D.; Kadirgama, K.

    2018-03-01

    Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process.

  17. What Fuzzy HOS May Mean

    DTIC Science & Technology

    1978-11-01

    a fuzzy set of real numbers clustered around m, or as a possibility distribution on the value of some ill-known quantity. A fuzzy relation R on the...distribution of . va.- nossibly clustered around some mean value. STho -Ict.n uf F to X is f. Moreover, It should be noticed that the image of a *uz!y L...10) 1(•)(y)= sup Rjin (i(x),• ()(y)) xcX One may verify that 11(-())(z)= sup min (V(x),N)( 0)(×)(z)) . Gmx ) xCX •) ’ . This shows that the extension

  18. Fuzzy logic particle tracking velocimetry

    NASA Technical Reports Server (NTRS)

    Wernet, Mark P.

    1993-01-01

    Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.

  19. Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

    NASA Astrophysics Data System (ADS)

    Li, X. L.; Zhao, Q. H.; Li, Y.

    2017-09-01

    Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

  20. A method and implementation for incorporating heuristic knowledge into a state estimator through the use of a fuzzy model

    NASA Astrophysics Data System (ADS)

    Swanson, Steven Roy

    The objective of the dissertation is to improve state estimation performance, as compared to a Kalman filter, when non-constant, or changing, biases exist in the measurement data. The state estimation performance increase will come from the use of a fuzzy model to determine the position and velocity gains of a state estimator. A method is proposed for incorporating heuristic knowledge into a state estimator through the use of a fuzzy model. This method consists of using a fuzzy model to determine the gains of the state estimator, converting the heuristic knowledge into the fuzzy model, and then optimizing the fuzzy model with a genetic algorithm. This method is applied to the problem of state estimation of a cascaded global positioning system (GPS)/inertial reference unit (IRU) navigation system. The GPS position data contains two major sources for position bias. The first bias is due to satellite errors and the second is due to the time delay or lag from when the GPS position is calculated until it is used in the state estimator. When a change in the bias of the measurement data occurs, a state estimator will converge on the new measurement data solution. This will introduce errors into a Kalman filter's estimated state velocities, which in turn will cause a position overshoot as it converges. By using a fuzzy model to determine the gains of a state estimator, the velocity errors and their associated deficiencies can be reduced.

  1. Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

    NASA Technical Reports Server (NTRS)

    Lea, Robert N. (Editor); Villarreal, James A. (Editor)

    1991-01-01

    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

  2. Learning and tuning fuzzy logic controllers through reinforcements.

    PubMed

    Berenji, H R; Khedkar, P

    1992-01-01

    A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  3. An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System

    PubMed Central

    Ni, Yepeng; Liu, Jianbo; Liu, Shan; Bai, Yaxin

    2016-01-01

    With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability. PMID:27618053

  4. A Model for the Development of Hospital Beds Using Fuzzy Analytical Hierarchy Process (Fuzzy AHP).

    PubMed

    Ravangard, Ramin; Bahadori, Mohammadkarim; Raadabadi, Mehdi; Teymourzadeh, Ehsan; Alimomohammadzadeh, Khalil; Mehrabian, Fardin

    2017-11-01

    This study aimed to identify and prioritize factors affecting the development of military hospital beds and provide a model using fuzzy analytical hierarchy process (Fuzzy AHP). This applied study was conducted in 2016 in Iran using a mixed method. The sample included experts in the field of military health care system. The MAXQDA 10.0 and Expert Choice 10.0 software were used for analyzing the collected data. Geographic situation, demographic status, economic status, health status, health care centers and organizations, financial and human resources, laws and regulations and by-laws, and the military nature of service recipients had effects on the development of military hospital beds. The military nature of service recipients (S=0.249) and economic status (S=0.040) received the highest and lowest priorities, respectively. Providing direct health care services to the military forces in order to maintain their dignity, and according to its effects in the crisis, as well as the necessity for maintaining the security of the armed forces, and the hospital beds per capita based on the existing laws, regulations and bylaws are of utmost importance.

  5. Support system for decision making in the identification of risk for body dysmorphic disorder: a fuzzy model.

    PubMed

    de Brito, Maria José Azevedo; Nahas, Fábio Xerfan; Ortega, Neli Regina Siqueira; Cordás, Táki Athanássios; Dini, Gal Moreira; Neto, Miguel Sabino; Ferreira, Lydia Masako

    2013-09-01

    To develop a fuzzy linguistic model to quantify the level of distress of patients seeking cosmetic surgery. Body dysmorphic disorder (BDD) is a mental condition related to body image relatively common among cosmetic surgery patients; it is difficult to diagnose and is a significant cause of morbidity and mortality. Fuzzy cognitive maps are an efficient tool based on human knowledge and experience that can handle uncertainty in identifying or grading BDD symptoms and the degree of body image dissatisfaction. Individuals who seek cosmetic procedures suffer from some degree of dissatisfaction with appearance. A fuzzy model was developed to measure distress levels in cosmetic surgery patients based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), diagnostic criterion B for BDD. We studied 288 patients of both sexes seeking abdominoplasty, rhinoplasty, or rhytidoplasty in a university hospital. Patient distress ranged from "none" to "severe" (range=7.5-31.6; cutoff point=18; area under the ROC curve=0.923). There was a significant agreement between the fuzzy model and DSM-IV criterion B (kappa=0.805; p<0.001). The fuzzy model measured distress levels with good accuracy, indicating that it can be used as a screening tool in cosmetic surgery and psychiatric practice. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  6. Fuzzy queries above relational database

    NASA Astrophysics Data System (ADS)

    Smolka, Pavel; Bradac, Vladimir

    2017-11-01

    The aim of the theme is to introduce a possibility of fuzzy queries implemented in relational databases. The issue is described on a model which identifies the appropriate part of the problem domain for fuzzy approach. The model is demonstrated on a database of wines focused on searching in it. The construction of the database complies with the Law of the Czech Republic.

  7. A mathematical model of neuro-fuzzy approximation in image classification

    NASA Astrophysics Data System (ADS)

    Gopalan, Sasi; Pinto, Linu; Sheela, C.; Arun Kumar M., N.

    2016-06-01

    Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.

  8. Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling.

    PubMed

    Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I; Kotsia, Anna P; Vakalis, Konstantinos V; Naka, Katerina K; Michalis, Lampros K

    2008-07-01

    A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

  9. Solving fully fuzzy transportation problem using pentagonal fuzzy numbers

    NASA Astrophysics Data System (ADS)

    Maheswari, P. Uma; Ganesan, K.

    2018-04-01

    In this paper, we propose a simple approach for the solution of fuzzy transportation problem under fuzzy environment in which the transportation costs, supplies at sources and demands at destinations are represented by pentagonal fuzzy numbers. The fuzzy transportation problem is solved without converting to its equivalent crisp form using a robust ranking technique and a new fuzzy arithmetic on pentagonal fuzzy numbers. To illustrate the proposed approach a numerical example is provided.

  10. Chaotic Motions in the Real Fuzzy Electronic Circuits

    DTIC Science & Technology

    2012-12-30

    field of secure communications, the original source should be blended with other complex signals. Chaotic signals are one of the good sources to be...Takagi-Sugeno (T-S) fuzzy chaotic systems on electronic circuit. In the research field of secure communications, the original source should be blended ...model. The overall fuzzy model of the system is achieved by fuzzy blending of the linear system models. Consider a continuous-time nonlinear dynamic

  11. Neurocontrol and fuzzy logic: Connections and designs

    NASA Technical Reports Server (NTRS)

    Werbos, Paul J.

    1991-01-01

    Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

  12. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics

    NASA Astrophysics Data System (ADS)

    Jahedi Rad, Shahpour; Kaveh, Mohammad; Sharabiani, Vali Rasooli; Taghinezhad, Ebrahim

    2018-05-01

    The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R 2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R 2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

  13. Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology.

    PubMed

    Motamedi, Shervin; Roy, Chandrabhushan; Shamshirband, Shahaboddin; Hashim, Roslan; Petković, Dalibor; Song, Ki-Il

    2015-08-01

    Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations. M.S. Thesis - MIT

    NASA Technical Reports Server (NTRS)

    Brown, Robert B.

    1994-01-01

    A software pilot model for Space Shuttle proximity operations is developed, utilizing fuzzy logic. The model is designed to emulate a human pilot during the terminal phase of a Space Shuttle approach to the Space Station. The model uses the same sensory information available to a human pilot and is based upon existing piloting rules and techniques determined from analysis of human pilot performance. Such a model is needed to generate numerous rendezvous simulations to various Space Station assembly stages for analysis of current NASA procedures and plume impingement loads on the Space Station. The advantages of a fuzzy logic pilot model are demonstrated by comparing its performance with NASA's man-in-the-loop simulations and with a similar model based upon traditional Boolean logic. The fuzzy model is shown to respond well from a number of initial conditions, with results typical of an average human. In addition, the ability to model different individual piloting techniques and new piloting rules is demonstrated.

  15. Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

    2016-11-01

    In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

  16. An Electromyographic-driven Musculoskeletal Torque Model using Neuro-Fuzzy System Identification: A Case Study

    PubMed Central

    Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza

    2014-01-01

    The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications. PMID:25426427

  17. Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS).

    PubMed

    Hosseini, Seyed Abolfazl; Esmaili Paeen Afrakoti, Iman

    2018-01-17

    The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology). Taking into account the normalization condition of each energy spectrum, 4300 neutron energy spectra were generated randomly. (The value in each bin was generated randomly, and finally a normalization of each generated energy spectrum was performed). The randomly generated neutron energy spectra were considered as output data of the developed ANFIS computational code in the training step. To calculate the neutron energy spectrum using conventional methods, an inverse problem with an approximately singular response matrix (with the determinant of the matrix close to zero) should be solved. The solution of the inverse problem using the conventional methods unfold neutron energy spectrum with low accuracy. Application of the iterative algorithms in the solution of such a problem, or utilizing the intelligent algorithms (in which there is no need to solve the problem), is usually preferred for unfolding of the energy spectrum. Therefore, the main reason for development of intelligent algorithms like ANFIS for unfolding of neutron energy spectra is to avoid solving the inverse problem. In the present study, the unfolded neutron energy spectra of 252Cf and 241Am-9Be neutron sources using the developed computational code were

  18. Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

    NASA Astrophysics Data System (ADS)

    Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

    2015-01-01

    The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and

  19. Multi-objective and Perishable Fuzzy Inventory Models Having Weibull Life-time With Time Dependent Demand, Demand Dependent Production and Time Varying Holding Cost: A Possibility/Necessity Approach

    NASA Astrophysics Data System (ADS)

    Pathak, Savita; Mondal, Seema Sarkar

    2010-10-01

    A multi-objective inventory model of deteriorating item has been developed with Weibull rate of decay, time dependent demand, demand dependent production, time varying holding cost allowing shortages in fuzzy environments for non- integrated and integrated businesses. Here objective is to maximize the profit from different deteriorating items with space constraint. The impreciseness of inventory parameters and goals for non-integrated business has been expressed by linear membership functions. The compromised solutions are obtained by different fuzzy optimization methods. To incorporate the relative importance of the objectives, the different cardinal weights crisp/fuzzy have been assigned. The models are illustrated with numerical examples and results of models with crisp/fuzzy weights are compared. The result for the model assuming them to be integrated business is obtained by using Generalized Reduced Gradient Method (GRG). The fuzzy integrated model with imprecise inventory cost is formulated to optimize the possibility necessity measure of fuzzy goal of the objective function by using credibility measure of fuzzy event by taking fuzzy expectation. The results of crisp/fuzzy integrated model are illustrated with numerical examples and results are compared.

  20. Design of fuzzy systems using neurofuzzy networks.

    PubMed

    Figueiredo, M; Gomide, F

    1999-01-01

    This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

  1. Fuzzy parametric uncertainty analysis of linear dynamical systems: A surrogate modeling approach

    NASA Astrophysics Data System (ADS)

    Chowdhury, R.; Adhikari, S.

    2012-10-01

    Uncertainty propagation engineering systems possess significant computational challenges. This paper explores the possibility of using correlated function expansion based metamodelling approach when uncertain system parameters are modeled using Fuzzy variables. In particular, the application of High-Dimensional Model Representation (HDMR) is proposed for fuzzy finite element analysis of dynamical systems. The HDMR expansion is a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior based on a hierarchy of functions of increasing dimensions. The input variables may be either finite-dimensional (i.e., a vector of parameters chosen from the Euclidean space RM) or may be infinite-dimensional as in the function space CM[0,1]. The computational effort to determine the expansion functions using the alpha cut method scales polynomially with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is integrated with a commercial Finite Element software. Modal analysis of a simplified aircraft wing with Fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations.

  2. Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

    ERIC Educational Resources Information Center

    Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

    2012-01-01

    With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

  3. LMI-based stability analysis of fuzzy-model-based control systems using approximated polynomial membership functions.

    PubMed

    Narimani, Mohammand; Lam, H K; Dilmaghani, R; Wolfe, Charles

    2011-06-01

    Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.

  4. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    PubMed

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  5. Encoding spatial images: A fuzzy set theory approach

    NASA Technical Reports Server (NTRS)

    Sztandera, Leszek M.

    1992-01-01

    As the use of fuzzy set theory continues to grow, there is an increased need for methodologies and formalisms to manipulate obtained fuzzy subsets. Concepts involving relative position of fuzzy patterns are acknowledged as being of high importance in many areas. In this paper, we present an approach based on the concept of dominance in fuzzy set theory for modelling relative positions among fuzzy subsets of a plane. In particular, we define the following spatial relations: to the left (right), in front of, behind, above, below, near, far from, and touching. This concept has been implemented to define spatial relationships among fuzzy subsets of the image plane. Spatial relationships based on fuzzy set theory, coupled with a fuzzy segmentation, should therefore yield realistic results in scene understanding.

  6. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    PubMed

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

  7. Connes distance function on fuzzy sphere and the connection between geometry and statistics

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Devi, Yendrembam Chaoba, E-mail: chaoba@bose.res.in; Chakraborty, Biswajit, E-mail: biswajit@bose.res.in; Prajapat, Shivraj, E-mail: shraprajapat@gmail.com

    An algorithm to compute Connes spectral distance, adaptable to the Hilbert-Schmidt operatorial formulation of non-commutative quantum mechanics, was developed earlier by introducing the appropriate spectral triple and used to compute infinitesimal distances in the Moyal plane, revealing a deep connection between geometry and statistics. In this paper, using the same algorithm, the Connes spectral distance has been calculated in the Hilbert-Schmidt operatorial formulation for the fuzzy sphere whose spatial coordinates satisfy the su(2) algebra. This has been computed for both the discrete and the Perelemov’s SU(2) coherent state. Here also, we get a connection between geometry and statistics which ismore » shown by computing the infinitesimal distance between mixed states on the quantum Hilbert space of a particular fuzzy sphere, indexed by n ∈ ℤ/2.« less

  8. Fuzzy time-series based on Fibonacci sequence for stock price forecasting

    NASA Astrophysics Data System (ADS)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

    2007-07-01

    Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

  9. Reliable Decentralized Control of Fuzzy Discrete-Event Systems and a Test Algorithm.

    PubMed

    Liu, Fuchun; Dziong, Zbigniew

    2013-02-01

    A framework for decentralized control of fuzzy discrete-event systems (FDESs) has been recently presented to guarantee the achievement of a given specification under the joint control of all local fuzzy supervisors. As a continuation, this paper addresses the reliable decentralized control of FDESs in face of possible failures of some local fuzzy supervisors. Roughly speaking, for an FDES equipped with n local fuzzy supervisors, a decentralized supervisor is called k-reliable (1 ≤ k ≤ n) provided that the control performance will not be degraded even when n - k local fuzzy supervisors fail. A necessary and sufficient condition for the existence of k-reliable decentralized supervisors of FDESs is proposed by introducing the notions of M̃uc-controllability and k-reliable coobservability of fuzzy language. In particular, a polynomial-time algorithm to test the k-reliable coobservability is developed by a constructive methodology, which indicates that the existence of k-reliable decentralized supervisors of FDESs can be checked with a polynomial complexity.

  10. Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

    NASA Astrophysics Data System (ADS)

    Valyrakis, Manousos; Zhang, Hanqing

    2014-05-01

    Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health of river systems but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation.

  11. A fuzzy classifier system for process control

    NASA Technical Reports Server (NTRS)

    Karr, C. L.; Phillips, J. C.

    1994-01-01

    A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

  12. A Model for the Development of Hospital Beds Using Fuzzy Analytical Hierarchy Process (Fuzzy AHP)

    PubMed Central

    RAVANGARD, Ramin; BAHADORI, Mohammadkarim; RAADABADI, Mehdi; TEYMOURZADEH, Ehsan; ALIMOMOHAMMADZADEH, Khalil; MEHRABIAN, Fardin

    2017-01-01

    Background: This study aimed to identify and prioritize factors affecting the development of military hospital beds and provide a model using fuzzy analytical hierarchy process (Fuzzy AHP). Methods: This applied study was conducted in 2016 in Iran using a mixed method. The sample included experts in the field of military health care system. The MAXQDA 10.0 and Expert Choice 10.0 software were used for analyzing the collected data. Results: Geographic situation, demographic status, economic status, health status, health care centers and organizations, financial and human resources, laws and regulations and by-laws, and the military nature of service recipients had effects on the development of military hospital beds. The military nature of service recipients (S=0.249) and economic status (S=0.040) received the highest and lowest priorities, respectively. Conclusion: Providing direct health care services to the military forces in order to maintain their dignity, and according to its effects in the crisis, as well as the necessity for maintaining the security of the armed forces, and the hospital beds per capita based on the existing laws, regulations and bylaws are of utmost importance. PMID:29167775

  13. Damage severity assessment in wind turbine blade laboratory model through fuzzy finite element model updating

    NASA Astrophysics Data System (ADS)

    Turnbull, Heather; Omenzetter, Piotr

    2017-04-01

    The recent shift towards development of clean, sustainable energy sources has provided a new challenge in terms of structural safety and reliability: with aging, manufacturing defects, harsh environmental and operational conditions, and extreme events such as lightning strikes wind turbines can become damaged resulting in production losses and environmental degradation. To monitor the current structural state of the turbine, structural health monitoring (SHM) techniques would be beneficial. Physics based SHM in the form of calibration of a finite element model (FEMs) by inverse techniques is adopted in this research. Fuzzy finite element model updating (FFEMU) techniques for damage severity assessment of a small-scale wind turbine blade are discussed and implemented. The main advantage is the ability of FFEMU to account in a simple way for uncertainty within the problem of model updating. Uncertainty quantification techniques, such as fuzzy sets, enable a convenient mathematical representation of the various uncertainties. Experimental frequencies obtained from modal analysis on a small-scale wind turbine blade were described by fuzzy numbers to model measurement uncertainty. During this investigation, damage severity estimation was investigated through addition of small masses of varying magnitude to the trailing edge of the structure. This structural modification, intended to be in lieu of damage, enabled non-destructive experimental simulation of structural change. A numerical model was constructed with multiple variable additional masses simulated upon the blades trailing edge and used as updating parameters. Objective functions for updating were constructed and minimized using both particle swarm optimization algorithm and firefly algorithm. FFEMU was able to obtain a prediction of baseline material properties of the blade whilst also successfully predicting, with sufficient accuracy, a larger magnitude of structural alteration and its location.

  14. A Fuzzy Robust Optimization Model for Waste Allocation Planning Under Uncertainty

    PubMed Central

    Xu, Ye; Huang, Guohe; Xu, Ling

    2014-01-01

    Abstract In this study, a fuzzy robust optimization (FRO) model was developed for supporting municipal solid waste management under uncertainty. The Development Zone of the City of Dalian, China, was used as a study case for demonstration. Comparing with traditional fuzzy models, the FRO model made improvement by considering the minimization of the weighted summation among the expected objective values, the differences between two extreme possible objective values, and the penalty of the constraints violation as the objective function, instead of relying purely on the minimization of expected value. Such an improvement leads to enhanced system reliability and the model becomes especially useful when multiple types of uncertainties and complexities are involved in the management system. Through a case study, the applicability of the FRO model was successfully demonstrated. Solutions under three future planning scenarios were provided by the FRO model, including (1) priority on economic development, (2) priority on environmental protection, and (3) balanced consideration for both. The balanced scenario solution was recommended for decision makers, since it respected both system economy and reliability. The model proved valuable in providing a comprehensive profile about the studied system and helping decision makers gain an in-depth insight into system complexity and select cost-effective management strategies. PMID:25317037

  15. A Fuzzy Robust Optimization Model for Waste Allocation Planning Under Uncertainty.

    PubMed

    Xu, Ye; Huang, Guohe; Xu, Ling

    2014-10-01

    In this study, a fuzzy robust optimization (FRO) model was developed for supporting municipal solid waste management under uncertainty. The Development Zone of the City of Dalian, China, was used as a study case for demonstration. Comparing with traditional fuzzy models, the FRO model made improvement by considering the minimization of the weighted summation among the expected objective values, the differences between two extreme possible objective values, and the penalty of the constraints violation as the objective function, instead of relying purely on the minimization of expected value. Such an improvement leads to enhanced system reliability and the model becomes especially useful when multiple types of uncertainties and complexities are involved in the management system. Through a case study, the applicability of the FRO model was successfully demonstrated. Solutions under three future planning scenarios were provided by the FRO model, including (1) priority on economic development, (2) priority on environmental protection, and (3) balanced consideration for both. The balanced scenario solution was recommended for decision makers, since it respected both system economy and reliability. The model proved valuable in providing a comprehensive profile about the studied system and helping decision makers gain an in-depth insight into system complexity and select cost-effective management strategies.

  16. Neural-Fuzzy model Based Steel Pipeline Multiple Cracks Classification

    NASA Astrophysics Data System (ADS)

    Elwalwal, Hatem Mostafa; Mahzan, Shahruddin Bin Hj.; Abdalla, Ahmed N.

    2017-10-01

    While pipes are cheaper than other means of transportation, this cost saving comes with a major price: pipes are subject to cracks, corrosion etc., which in turn can cause leakage and environmental damage. In this paper, Neural-Fuzzy model for multiple cracks classification based on Lamb Guide Wave. Simulation results for 42 sample were collected using ANSYS software. The current research object to carry on the numerical simulation and experimental study, aiming at finding an effective way to detection and the localization of cracks and holes defects in the main body of pipeline. Considering the damage form of multiple cracks and holes which may exist in pipeline, to determine the respective position in the steel pipe. In addition, the technique used in this research a guided lamb wave based structural health monitoring method whereas piezoelectric transducers will use as exciting and receiving sensors by Pitch-Catch method. Implementation of simple learning mechanism has been developed specially for the ANN for fuzzy the system represented.

  17. A two-phased fuzzy decision making procedure for IT supplier selection

    NASA Astrophysics Data System (ADS)

    Shohaimay, Fairuz; Ramli, Nazirah; Mohamed, Siti Rosiah; Mohd, Ainun Hafizah

    2013-09-01

    In many studies on fuzzy decision making, linguistic terms are usually represented by corresponding fixed triangular or trapezoidal fuzzy numbers. However, the fixed fuzzy numbers used in decision making process may not explain the actual respondents' opinions. Hence, a two-phased fuzzy decision making procedure is proposed. First, triangular fuzzy numbers were built based on respondents' opinions on the appropriate range (0-100) for each seven-scale linguistic terms. Then, the fuzzy numbers were integrated into fuzzy decision making model. The applicability of the proposed method is demonstrated in a case study of supplier selection in Information Technology (IT) department. The results produced via the developed fuzzy numbers were consistent with the results obtained using fixed fuzzy numbers. However, with different set of fuzzy numbers based on respondents, there is a difference in the ranking of suppliers based on criterion X1 (background of supplier). Hopefully the proposed model which incorporates fuzzy numbers based on respondents will provide a more significant meaning towards future decision making.

  18. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  19. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  20. Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

    PubMed

    Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin

    2014-01-01

    Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  1. Automatic approach to deriving fuzzy slope positions

    NASA Astrophysics Data System (ADS)

    Zhu, Liang-Jun; Zhu, A.-Xing; Qin, Cheng-Zhi; Liu, Jun-Zhi

    2018-03-01

    Fuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions.

  2. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

    PubMed

    Yang, Shiju; Li, Chuandong; Huang, Tingwen

    2016-03-01

    The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Asymmetric Fuzzy Control of a Positive and Negative Pneumatic Pressure Servo System

    NASA Astrophysics Data System (ADS)

    Yang, Gang; Du, Jing-Min; Fu, Xiao-Yun; Li, Bao-Ren

    2017-11-01

    The pneumatic pressure control systems have been used in some fields. However, the researches on pneumatic pressure control mainly focus on constant pressure regulation. Poor dynamic characteristics and strong nonlinearity of such systems limit its application in the field of pressure tracking control. In order to meet the demand of generating dynamic pressure signal in the application of the hardware-in-the-loop simulation of aerospace engineering, a positive and negative pneumatic pressure servo system is provided to implement dynamic adjustment of sealed chamber pressure. A mathematical model is established with simulation and experiment being implemented afterwards to discuss the characteristics of the system, which shows serious asymmetry in the process of charging and discharging. Based on the analysis of the system dynamics, a fuzzy proportional integral derivative (PID) controller with asymmetric fuzzy compensator is proposed. Different from conventional adjusting mechanisms employing the error and change in error of the controlled variable as input parameters, the current chamber pressure and charging or discharging state are chosen as inputs of the compensator, which improves adaptability. To verify the effectiveness and performance of the proposed controller, the comparison experiments tracking sinusoidal and square wave commands are conducted. Experimental results show that the proposed controller can obtain better dynamic performance and relatively consistent control performance across the scope of work (2-140 kPa). The research proposes a fuzzy control method to overcome asymmetry and enhance adaptability for the positive and negative pneumatic pressure servo system.

  4. New similarity of triangular fuzzy number and its application.

    PubMed

    Zhang, Xixiang; Ma, Weimin; Chen, Liping

    2014-01-01

    The similarity of triangular fuzzy numbers is an important metric for application of it. There exist several approaches to measure similarity of triangular fuzzy numbers. However, some of them are opt to be large. To make the similarity well distributed, a new method SIAM (Shape's Indifferent Area and Midpoint) to measure triangular fuzzy number is put forward, which takes the shape's indifferent area and midpoint of two triangular fuzzy numbers into consideration. Comparison with other similarity measurements shows the effectiveness of the proposed method. Then, it is applied to collaborative filtering recommendation to measure users' similarity. A collaborative filtering case is used to illustrate users' similarity based on cloud model and triangular fuzzy number; the result indicates that users' similarity based on triangular fuzzy number can obtain better discrimination. Finally, a simulated collaborative filtering recommendation system is developed which uses cloud model and triangular fuzzy number to express users' comprehensive evaluation on items, and result shows that the accuracy of collaborative filtering recommendation based on triangular fuzzy number is higher.

  5. The coordinating contracts of supply chain in a fuzzy decision environment.

    PubMed

    Sang, Shengju

    2016-01-01

    The rapid change of the product life cycle is making the parameters of the supply chain models more and more uncertain. Therefore, we consider the coordination mechanisms between one manufacturer and one retailer in a fuzzy decision marking environment, where the parameters of the models can be forecasted and expressed as the triangular fuzzy variables. The centralized decision-making system, two types of supply chain contracts, namely, the revenue sharing contract and the return contract are proposed. To obtain their optimal policies, the fuzzy set theory is adopted to solve these fuzzy models. Finally, three numerical examples are provided to analyze the impacts of the fuzziness of the market demand, retail price and salvage value of the product on the optimal solutions in two contracts. It shows that in order to obtain more fuzzy expected profits the retailer and the manufacturer should seek as low fuzziness of demand, high fuzziness of the retail price and the salvage value as possible in both contracts.

  6. Fuzzy Constraint Based Model for Efficient Management of Dynamic Purchasing Environments

    NASA Astrophysics Data System (ADS)

    Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

    2007-12-01

    This paper considers the application of a fuzzy constraint based model for handling dynamic environments where only one of possibly many bundles of items must be purchased and quotes for items open and close over time. Simulation results are presented and compared with the optimal solution.

  7. Fuzzy regulator design for wind turbine yaw control.

    PubMed

    Theodoropoulos, Stefanos; Kandris, Dionisis; Samarakou, Maria; Koulouras, Grigorios

    2014-01-01

    This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness.

  8. Designing a Successful Bidding Strategy Using Fuzzy Sets and Agent Attitudes

    NASA Astrophysics Data System (ADS)

    Ma, Jun; Goyal, Madhu Lata

    To be successful in a multi-attribute auction, agents must be capable of adapting to continuously changing bidding price. This chapter presents a novel fuzzy attitude-based bidding strategy (FA-Bid), which employs dual assessment technique, i.e., assessment of multiple attributes of the goods as well as assessment of agents' attitude (eagerness) to procure an item in automated auction. The assessment of attributes adapts the fuzzy sets technique to handle uncertainty of the bidding process as well use heuristic rules to determine the attitude of bidding agents in simulated auctions to procure goods. The overall assessment is used to determine a price range based on current bid, which finally selects the best one as the new bid.

  9. Real-time fuzzy inference based robot path planning

    NASA Technical Reports Server (NTRS)

    Pacini, Peter J.; Teichrow, Jon S.

    1990-01-01

    This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.

  10. Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon.

    PubMed

    Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F

    2014-10-15

    In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Optoelectronic fuzzy associative memory with controllable attraction basin sizes

    NASA Astrophysics Data System (ADS)

    Wen, Zhiqing; Campbell, Scott; Wu, Weishu; Yeh, Pochi

    1995-10-01

    We propose and demonstrate a new fuzzy associative memory model that provides an option to control the sizes of the attraction basins in neural networks. In our optoelectronic implementation we use spatial/polarization encoding to represent the fuzzy variables. Shadow casting of the encoded patterns is employed to yield the fuzzy-absolute difference between fuzzy variables.

  12. Regional Management of an Aquifer for Mining Under Fuzzy Environmental Objectives

    NASA Astrophysics Data System (ADS)

    BogáRdi, IstváN.; BáRdossy, AndráS.; Duckstein, Lucien

    1983-12-01

    A methodology is developed for the dynamic multiobjective management of a multipurpose regional aquifer. In a case study of bauxite mining in Western Hungary, ore deposits are often under the piezometric level of a karstic aquifer, while this same aquifer also provides recharge flows for thermal springs. N + 1 objectives are to be minimized, the first one being total discounted cost of control by dewatering or grouting; the other N objectives consist of the flow of thermal springs at N control points. However, there is no agreement among experts as to a set of numerical values that would constitute a "sound environment"; for this reason a fuzzy set analysis is used, and the N environmental objectives are combined into a single fuzzy membership function. The constraints include ore availability, various capacities, and the state transition function that describes the behavior of both piezometric head and underground flow. The model is linearized and solved as a biobjective dynamic program by using multiobjective compromise programming. A numerical example with N = 2 appears to lead to realistic control policies. Extension of the model to the nonlinear case is discussed.

  13. Multiple flood vulnerability assessment approach based on fuzzy comprehensive evaluation method and coordinated development degree model.

    PubMed

    Yang, Weichao; Xu, Kui; Lian, Jijian; Bin, Lingling; Ma, Chao

    2018-05-01

    Flood is a serious challenge that increasingly affects the residents as well as policymakers. Flood vulnerability assessment is becoming gradually relevant in the world. The purpose of this study is to develop an approach to reveal the relationship between exposure, sensitivity and adaptive capacity for better flood vulnerability assessment, based on the fuzzy comprehensive evaluation method (FCEM) and coordinated development degree model (CDDM). The approach is organized into three parts: establishment of index system, assessment of exposure, sensitivity and adaptive capacity, and multiple flood vulnerability assessment. Hydrodynamic model and statistical data are employed for the establishment of index system; FCEM is used to evaluate exposure, sensitivity and adaptive capacity; and CDDM is applied to express the relationship of the three components of vulnerability. Six multiple flood vulnerability types and four levels are proposed to assess flood vulnerability from multiple perspectives. Then the approach is applied to assess the spatiality of flood vulnerability in Hainan's eastern area, China. Based on the results of multiple flood vulnerability, a decision-making process for rational allocation of limited resources is proposed and applied to the study area. The study shows that multiple flood vulnerability assessment can evaluate vulnerability more completely, and help decision makers learn more information about making decisions in a more comprehensive way. In summary, this study provides a new way for flood vulnerability assessment and disaster prevention decision. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

    NASA Astrophysics Data System (ADS)

    Kothari, Mahesh; Gharde, K. D.

    2015-07-01

    The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

  15. Fuzzy scalar and vector median filters based on fuzzy distances.

    PubMed

    Chatzis, V; Pitas, I

    1999-01-01

    In this paper, the fuzzy scalar median (FSM) is proposed, defined by using ordering of fuzzy numbers based on fuzzy minimum and maximum operations defined by using the extension principle. Alternatively, the FSM is defined from the minimization of a fuzzy distance measure, and the equivalence of the two definitions is proven. Then, the fuzzy vector median (FVM) is proposed as an extension of vector median, based on a novel distance definition of fuzzy vectors, which satisfy the property of angle decomposition. By defining properly the fuzziness of a value, the combination of the basic properties of the classical scalar and vector median (VM) filter with other desirable characteristics can be succeeded.

  16. Complex Fuzzy Set-Valued Complex Fuzzy Measures and Their Properties

    PubMed Central

    Ma, Shengquan; Li, Shenggang

    2014-01-01

    Let F*(K) be the set of all fuzzy complex numbers. In this paper some classical and measure-theoretical notions are extended to the case of complex fuzzy sets. They are fuzzy complex number-valued distance on F*(K), fuzzy complex number-valued measure on F*(K), and some related notions, such as null-additivity, pseudo-null-additivity, null-subtraction, pseudo-null-subtraction, autocontionuous from above, autocontionuous from below, and autocontinuity of the defined fuzzy complex number-valued measures. Properties of fuzzy complex number-valued measures are studied in detail. PMID:25093202

  17. Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

    NASA Astrophysics Data System (ADS)

    Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

    2016-06-01

    The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

  18. A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken

    This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology,more » comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)« less

  19. Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

    NASA Astrophysics Data System (ADS)

    Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

    2015-01-01

    This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

  20. Face recognition using total margin-based adaptive fuzzy support vector machines.

    PubMed

    Liu, Yi-Hung; Chen, Yen-Ting

    2007-01-01

    This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained.

  1. Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1

    NASA Technical Reports Server (NTRS)

    Lea, Robert N. (Editor); Villarreal, James (Editor)

    1991-01-01

    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

  2. Fuzzy Current-Mode Control and Stability Analysis

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    2000-01-01

    In this paper a current-mode control (CMC) methodology is developed for a buck converter by using a fuzzy logic controller. Conventional CMC methodologies are based on lead-lag compensation with voltage and inductor current feedback. In this paper the converter lead-lag compensation will be substituted with a fuzzy controller. A small-signal model of the fuzzy controller will also be developed in order to examine the stability properties of this buck converter control system. The paper develops an analytical approach, introducing fuzzy control into the area of CMC.

  3. Measuring Distance of Fuzzy Numbers by Trapezoidal Fuzzy Numbers

    NASA Astrophysics Data System (ADS)

    Hajjari, Tayebeh

    2010-11-01

    Fuzzy numbers and more generally linguistic values are approximate assessments, given by experts and accepted by decision-makers when obtaining value that is more accurate is impossible or unnecessary. Distance between two fuzzy numbers plays an important role in linguistic decision-making. It is reasonable to define a fuzzy distance between fuzzy objects. To achieve this aim, the researcher presents a new distance measure for fuzzy numbers by means of improved centroid distance method. The metric properties are also studied. The advantage is the calculation of the proposed method is far simple than previous approaches.

  4. Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model

    PubMed Central

    An, Yan; Zou, Zhihong; Li, Ranran

    2014-01-01

    A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD5, NH3-N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD5, NH3-N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system. PMID:24675643

  5. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

    NASA Astrophysics Data System (ADS)

    Kurtulus, Bedri; Razack, Moumtaz

    2010-02-01

    SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

  6. A stochastic HMM-based forecasting model for fuzzy time series.

    PubMed

    Li, Sheng-Tun; Cheng, Yi-Chung

    2010-10-01

    Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

  7. Takagi-Sugeno fuzzy model based robust dissipative control for uncertain flexible spacecraft with saturated time-delay input.

    PubMed

    Xu, Shidong; Sun, Guanghui; Sun, Weichao

    2017-01-01

    In this paper, the problem of robust dissipative control is investigated for uncertain flexible spacecraft based on Takagi-Sugeno (T-S) fuzzy model with saturated time-delay input. Different from most existing strategies, T-S fuzzy approximation approach is used to model the nonlinear dynamics of flexible spacecraft. Simultaneously, the physical constraints of system, like input delay, input saturation, and parameter uncertainties, are also taken care of in the fuzzy model. By employing Lyapunov-Krasovskii method and convex optimization technique, a novel robust controller is proposed to implement rest-to-rest attitude maneuver for flexible spacecraft, and the guaranteed dissipative performance enables the uncertain closed-loop system to reject the influence of elastic vibrations and external disturbances. Finally, an illustrative design example integrated with simulation results are provided to confirm the applicability and merits of the developed control strategy. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Directed Laplacians For Fuzzy Autocatalytic Set Of Fuzzy Graph Type-3 Of An Incineration Process

    NASA Astrophysics Data System (ADS)

    Ahmad, Tahir; Baharun, Sabariah; Bakar, Sumarni Abu

    2010-11-01

    Fuzzy Autocatalytic Set (FACS) of Fuzzy Graph Type-3 was used in the modeling of a clinical waste incineration process in Malacca. FACS provided more accurate explanations of the incineration process than using crisp graph. In this paper we explore further FACS. Directed and combinatorial Laplacian of FACS are developed and their basic properties are presented.

  9. Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive.

    PubMed

    Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S

    2015-07-01

    In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  10. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

    PubMed

    Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

    2015-01-01

    Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

  11. Fuzzy logic in control systems: Fuzzy logic controller. I, II

    NASA Technical Reports Server (NTRS)

    Lee, Chuen Chien

    1990-01-01

    Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.

  12. Fuzzy Logic-Based Filter for Removing Additive and Impulsive Noise from Color Images

    NASA Astrophysics Data System (ADS)

    Zhu, Yuhong; Li, Hongyang; Jiang, Huageng

    2017-12-01

    This paper presents an efficient filter method based on fuzzy logics for adaptively removing additive and impulsive noise from color images. The proposed filter comprises two parts including noise detection and noise removal filtering. In the detection part, the fuzzy peer group concept is applied to determine what type of noise is added to each pixel of the corrupted image. In the filter part, the impulse noise is deducted by the vector median filter in the CIELAB color space and an optimal fuzzy filter is introduced to reduce the Gaussian noise, while they can work together to remove the mixed Gaussian-impulse noise from color images. Experimental results on several color images proves the efficacy of the proposed fuzzy filter.

  13. A Combination of Extended Fuzzy AHP and Fuzzy GRA for Government E-Tendering in Hybrid Fuzzy Environment

    PubMed Central

    Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

    2014-01-01

    The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach. PMID:25057506

  14. A combination of extended fuzzy AHP and fuzzy GRA for government E-tendering in hybrid fuzzy environment.

    PubMed

    Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

    2014-01-01

    The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach.

  15. Fuzzy associative memories

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1991-01-01

    Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

  16. Fuzzy bi-objective linear programming for portfolio selection problem with magnitude ranking function

    NASA Astrophysics Data System (ADS)

    Kusumawati, Rosita; Subekti, Retno

    2017-04-01

    Fuzzy bi-objective linear programming (FBOLP) model is bi-objective linear programming model in fuzzy number set where the coefficients of the equations are fuzzy number. This model is proposed to solve portfolio selection problem which generate an asset portfolio with the lowest risk and the highest expected return. FBOLP model with normal fuzzy numbers for risk and expected return of stocks is transformed into linear programming (LP) model using magnitude ranking function.

  17. Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

    NASA Astrophysics Data System (ADS)

    Farahi Ghasre Aboonasr, Sedigheh; Zamani, Ahmad; Razavipour, Fatemeh; Boostani, Reza

    2017-08-01

    Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.

  18. Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

    NASA Technical Reports Server (NTRS)

    Culbert, Christopher J. (Editor)

    1993-01-01

    Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

  19. Incomplete fuzzy data processing systems using artificial neural network

    NASA Technical Reports Server (NTRS)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  20. Measuring Household Vulnerability: A Fuzzy Approach

    NASA Astrophysics Data System (ADS)

    Sethi, G.; Pierce, S. A.

    2016-12-01

    This research develops an index of vulnerability for Ugandan households using a variety of economic, social and environmental variables with two objectives. First, there is only a small body of research that measures household vulnerability. Given the stresses faced by households susceptible to water, environment, food, livelihood, energy, and health security concerns, it is critical that they be identified in order to make effective policy. We draw on the socio-ecological systems (SES) framework described by Ostrom (2009) and adapt the model developed by from Giupponi, Giove, and Giannini (2013) to develop a composite measure. Second, most indices in the literature are linear in nature, relying on simple weighted averages. In this research, we contrast the results obtained by a simple weighted average with those obtained by using the Choquet integral. The Choquet integral is a fuzzy measure, and is based on the generalization of the Lebesgue integral. Due to its non-additive nature, the Choquet integral offers a more general approach. Our results reveal that all households included in this study are highly vulnerable, and that vulnerability scores obtained by the fuzzy approach are significantly different from those obtained by using the simple weighted average (p = 9.46e-160).

  1. Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine

    PubMed Central

    Shamshirband, Shahaboddin; Petković, Dalibor; Hashim, Roslan; Motamedi, Shervin

    2014-01-01

    Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621

  2. Fuzzy logic modeling of high performance rechargeable batteries

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Singh, P.; Fennie, C. Jr.; Reisner, D.E.

    1998-07-01

    Accurate battery state-of-charge (SOC) measurements are critical in many portable electronic device applications. Yet conventional techniques for battery SOC estimation are limited in their accuracy, reliability, and flexibility. In this paper the authors present a powerful new approach to estimate battery SOC using a fuzzy logic-based methodology. This approach provides a universally applicable, accurate method for battery SOC estimation either integrated within, or as an external monitor to, an electronic device. The methodology is demonstrated in modeling impedance measurements on Ni-MH cells and discharge voltage curves of Li-ion cells.

  3. Expected value based fuzzy programming approach to solve integrated supplier selection and inventory control problem with fuzzy demand

    NASA Astrophysics Data System (ADS)

    Sutrisno; Widowati; Sunarsih; Kartono

    2018-01-01

    In this paper, a mathematical model in quadratic programming with fuzzy parameter is proposed to determine the optimal strategy for integrated inventory control and supplier selection problem with fuzzy demand. To solve the corresponding optimization problem, we use the expected value based fuzzy programming. Numerical examples are performed to evaluate the model. From the results, the optimal amount of each product that have to be purchased from each supplier for each time period and the optimal amount of each product that have to be stored in the inventory for each time period were determined with minimum total cost and the inventory level was sufficiently closed to the reference level.

  4. Fuzzy Regulator Design for Wind Turbine Yaw Control

    PubMed Central

    Koulouras, Grigorios

    2014-01-01

    This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness. PMID:24693237

  5. Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

    PubMed

    Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

    2013-01-01

    An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R (2)). Graphical plots were also used for model comparison. The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

  6. Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems

    PubMed Central

    Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

    2013-01-01

    Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R 2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

  7. Adaptability in linkage of soil carbon nutrient cycles - the SEAM model

    NASA Astrophysics Data System (ADS)

    Wutzler, Thomas; Zaehle, Sönke; Schrumpf, Marion; Ahrens, Bernhard; Reichstein, Markus

    2017-04-01

    In order to understand the coupling of carbon (C) and nitrogen (N) cycles, it is necessary to understand C and N-use efficiencies of microbial soil organic matter (SOM) decomposition. While important controls of those efficiencies by microbial community adaptations have been shown at the scale of a soil pore, an abstract simplified representation of community adaptations is needed at ecosystem scale. Therefore we developed the soil enzyme allocation model (SEAM), which takes a holistic, partly optimality based approach to describe C and N dynamics at the spatial scale of an ecosystem and time-scales of years and longer. We explicitly modelled community adaptation strategies of resource allocation to extracellular enzymes and enzyme limitations on SOM decomposition. Using SEAM, we explored whether alternative strategy-hypotheses can have strong effects on SOM and inorganic N cycling. Results from prototypical simulations and a calibration to observations of an intensive pasture site showed that the so-called revenue enzyme allocation strategy was most viable. This strategy accounts for microbial adaptations to both, stoichiometry and amount of different SOM resources, and supported the largest microbial biomass under a wide range of conditions. Predictions of the SEAM model were qualitatively similar to models explicitly representing competing microbial groups. With adaptive enzyme allocation under conditions of high C/N ratio of litter inputs, N in formerly locked in slowly degrading SOM pools was made accessible, whereas with high N inputs, N was sequestered in SOM and protected from leaching. The finding that adaptation in enzyme allocation changes C and N-use efficiencies of SOM decomposition implies that concepts of C-nutrient cycle interactions should take account for the effects of such adaptations. This can be done using a holistic optimality approach.

  8. A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

    NASA Astrophysics Data System (ADS)

    Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

    2009-11-01

    Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

  9. Assessment and prediction of air quality using fuzzy logic and autoregressive models

    NASA Astrophysics Data System (ADS)

    Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

    2012-12-01

    In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

  10. Self-learning fuzzy controllers based on temporal back propagation

    NASA Technical Reports Server (NTRS)

    Jang, Jyh-Shing R.

    1992-01-01

    This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

  11. Pricing for a basket of LCDS under fuzzy environments.

    PubMed

    Wu, Liang; Liu, Jie-Fang; Wang, Jun-Tao; Zhuang, Ya-Ming

    2016-01-01

    This paper looks at both the prepayment risks of housing mortgage loan credit default swaps (LCDS) as well as the fuzziness and hesitation of investors as regards prepayments by borrowers. It further discusses the first default pricing of a basket of LCDS in a fuzzy environment by using stochastic analysis and triangular intuition-based fuzzy set theory. Through the 'fuzzification' of the sensitivity coefficient in the prepayment intensity, this paper describes the dynamic features of mortgage housing values using the One-factor copula function and concludes with a formula for 'fuzzy' pricing the first default of a basket of LCDS. Using analog simulation to analyze the sensitivity of hesitation, we derive a model that considers what the LCDS fair premium is in a fuzzy environment, including a pure random environment. In addition, the model also shows that a suitable pricing range will give investors more flexible choices and make the predictions of the model closer to real market values.

  12. Computerized decision support system for mass identification in breast using digital mammogram: a study on GA-based neuro-fuzzy approaches.

    PubMed

    Das, Arpita; Bhattacharya, Mahua

    2011-01-01

    In the present work, authors have developed a treatment planning system implementing genetic based neuro-fuzzy approaches for accurate analysis of shape and margin of tumor masses appearing in breast using digital mammogram. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology, genetic algorithm (GA) has been used for searching of effective input feature vectors combined with adaptive neuro-fuzzy model for final classification of different boundaries of tumor masses. The study involves 200 digitized mammograms from MIAS and other databases and has shown 86% correct classification rate.

  13. Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

    2009-04-01

    Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

  14. Modeling urban air pollution with optimized hierarchical fuzzy inference system.

    PubMed

    Tashayo, Behnam; Alimohammadi, Abbas

    2016-10-01

    Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

  15. An improved advertising CTR prediction approach based on the fuzzy deep neural network.

    PubMed

    Jiang, Zilong; Gao, Shu; Li, Mingjiang

    2018-01-01

    Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

  16. Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

    NASA Astrophysics Data System (ADS)

    El-Sebakhy, Emad A.

    2009-09-01

    Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

  17. Water quality assessment of the Li Canal using a functional fuzzy synthetic evaluation model.

    PubMed

    Feng, Yan; Ling, Liu

    2014-07-01

    Through introducing functional data analysis (FDA) theory into the conventional fuzzy synthetic evaluation (FSE) method, the functional fuzzy synthetic evaluation (FFSE) model is established. FFSE keeps the property of the conventional FSE that the fuzziness in the water quality condition can be suitably measured. Furthermore, compared with FSE, FFSE has the following advantages: (1) FFSE requires fewer conditions for observation, for example, pollutants can be monitored at different times, and missing data is accepted; (2) the dynamic variation of the water quality condition can be represented more comprehensively and intuitively. The procedure of FFSE is discussed and the water quality of the Li Canal in 2012 is evaluated as an illustration. The synthetic classification of the Li Canal is "II" in January, February and July, and "I" in other months, which can satisfy the requirement of the Chinese South-to-North Water Diversion Project.

  18. Dynamic Assessment of Water Quality Based on a Variable Fuzzy Pattern Recognition Model

    PubMed Central

    Xu, Shiguo; Wang, Tianxiang; Hu, Suduan

    2015-01-01

    Water quality assessment is an important foundation of water resource protection and is affected by many indicators. The dynamic and fuzzy changes of water quality lead to problems for proper assessment. This paper explores a method which is in accordance with the water quality changes. The proposed method is based on the variable fuzzy pattern recognition (VFPR) model and combines the analytic hierarchy process (AHP) model with the entropy weight (EW) method. The proposed method was applied to dynamically assess the water quality of Biliuhe Reservoir (Dailan, China). The results show that the water quality level is between levels 2 and 3 and worse in August or September, caused by the increasing water temperature and rainfall. Weights and methods are compared and random errors of the values of indicators are analyzed. It is concluded that the proposed method has advantages of dynamism, fuzzification and stability by considering the interval influence of multiple indicators and using the average level characteristic values of four models as results. PMID:25689998

  19. Dynamic assessment of water quality based on a variable fuzzy pattern recognition model.

    PubMed

    Xu, Shiguo; Wang, Tianxiang; Hu, Suduan

    2015-02-16

    Water quality assessment is an important foundation of water resource protection and is affected by many indicators. The dynamic and fuzzy changes of water quality lead to problems for proper assessment. This paper explores a method which is in accordance with the water quality changes. The proposed method is based on the variable fuzzy pattern recognition (VFPR) model and combines the analytic hierarchy process (AHP) model with the entropy weight (EW) method. The proposed method was applied to dynamically assess the water quality of Biliuhe Reservoir (Dailan, China). The results show that the water quality level is between levels 2 and 3 and worse in August or September, caused by the increasing water temperature and rainfall. Weights and methods are compared and random errors of the values of indicators are analyzed. It is concluded that the proposed method has advantages of dynamism, fuzzification and stability by considering the interval influence of multiple indicators and using the average level characteristic values of four models as results.

  20. Model predictive controller design for boost DC-DC converter using T-S fuzzy cost function

    NASA Astrophysics Data System (ADS)

    Seo, Sang-Wha; Kim, Yong; Choi, Han Ho

    2017-11-01

    This paper proposes a Takagi-Sugeno (T-S) fuzzy method to select cost function weights of finite control set model predictive DC-DC converter control algorithms. The proposed method updates the cost function weights at every sample time by using T-S type fuzzy rules derived from the common optimal control engineering knowledge that a state or input variable with an excessively large magnitude can be penalised by increasing the weight corresponding to the variable. The best control input is determined via the online optimisation of the T-S fuzzy cost function for all the possible control input sequences. This paper implements the proposed model predictive control algorithm in real time on a Texas Instruments TMS320F28335 floating-point Digital Signal Processor (DSP). Some experimental results are given to illuminate the practicality and effectiveness of the proposed control system under several operating conditions. The results verify that our method can yield not only good transient and steady-state responses (fast recovery time, small overshoot, zero steady-state error, etc.) but also insensitiveness to abrupt load or input voltage parameter variations.