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Sample records for international fuzzy systems

  1. Fuzzy expert systems using CLIPS

    NASA Technical Reports Server (NTRS)

    Le, Thach C.

    1994-01-01

    This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.

  2. Distributed fuzzy system modeling

    SciTech Connect

    Pedrycz, W.; Chi Fung Lam, P.; Rocha, A.F.

    1995-05-01

    The paper introduces and studies an idea of distributed modeling treating it as a new paradigm of fuzzy system modeling and analysis. This form of modeling is oriented towards developing individual (local) fuzzy models for specific modeling landmarks (expressed as fuzzy sets) and determining the essential logical relationships between these local models. The models themselves are implemented in the form of logic processors being regarded as specialized fuzzy neural networks. The interaction between the processors is developed either in an inhibitory or excitatory way. In more descriptive way, the distributed model can be sought as a collection of fuzzy finite state machines with their individual local first or higher order memories. It is also clarified how the concept of distributed modeling narrows down a gap between purely numerical (quantitative) models and the qualitative ones originated within the realm of Artificial Intelligence. The overall architecture of distributed modeling is discussed along with the detailed learning schemes. The results of extensive simulation experiments are provided as well. 17 refs.

  3. Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Takagi, Hideyuki

    1993-12-01

    This paper overviews four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to auto-design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to auto-design fuzzy systems, and (4) a fuzzy knowledge-based system to control genetic parameter dynamically.

  4. Learning fuzzy logic control system

    NASA Technical Reports Server (NTRS)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  5. Evaluation of fuzzy inference systems using fuzzy least squares

    NASA Technical Reports Server (NTRS)

    Barone, Joseph M.

    1992-01-01

    Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

  6. 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.

  7. Fuzzy logic control and optimization system

    DOEpatents

    Lou, Xinsheng [West Hartford, CT

    2012-04-17

    A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

  8. Fuzzy logic control for camera tracking system

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

    1992-01-01

    A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

  9. 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.

  10. Minimal Solution of Singular LR Fuzzy Linear Systems

    PubMed Central

    Nikuie, M.; Ahmad, M. Z.

    2014-01-01

    In this paper, the singular LR fuzzy linear system is introduced. Such systems are divided into two parts: singular consistent LR fuzzy linear systems and singular inconsistent LR fuzzy linear systems. The capability of the generalized inverses such as Drazin inverse, pseudoinverse, and {1}-inverse in finding minimal solution of singular consistent LR fuzzy linear systems is investigated. PMID:24737977

  11. 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.

  12. 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.

  13. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning for pitch control system

    NASA Astrophysics Data System (ADS)

    Li, Yezi; Xiao, Cheng; Sun, Jinhao

    2013-03-01

    PID and fuzzy PID controller are applied into the pitch control system. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist.

  14. Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.

    PubMed

    Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu

    2015-05-01

    This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.

  15. Single board system for fuzzy inference

    NASA Technical Reports Server (NTRS)

    Symon, James R.; Watanabe, Hiroyuki

    1991-01-01

    The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

  16. 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.

  17. Diagnosing Parkinson's Diseases Using Fuzzy Neural System

    PubMed Central

    Abiyev, Rahib H.; Abizade, Sanan

    2016-01-01

    This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson's disease. A diagnosing of Parkinson's diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. PMID:26881009

  18. Non-stationary stochastic vibration analysis of fuzzy truss system

    NASA Astrophysics Data System (ADS)

    Ma, Juan; Chen, Jian-jun; Gao, Wei; Zhai, Tian-song

    2006-11-01

    A new method (fuzzy factor method based on the fuzzy sets theory) for the dynamic response analysis of fuzzy truss system under non-stationary stochastic excitation is presented. Considering the fuzziness of the structural physical parameters and geometric dimensions simultaneously, the fuzzy correlation function matrix of the structural displacement response in time domain is derived using the fuzzy factor method and the optimisation method; then from the structural non-stationary stochastic response in the frequency domain, the fuzzy mean square values of the displacement and stress response are developed by the fuzzy factor method. The influences of the fuzziness of the physical parameters and geometric dimensions on the fuzziness of the mean square values of the structural displacement and stress response are illustrated via two engineering examples and some important conclusions are obtained.

  19. A Fuzzy Control Irrigation System For Cottonfield

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Zhao, Yandong; Wang, Yiming; Li, Jinping

    A fuzzy control irrigation system for cotton field is presented in this paper. The system is composed of host computer, slave computer controller, communication module, soil water sensors, valve controllers, and system software. A fuzzy control model is constructed to control the irrigation time and irrigation quantity for cotton filed. According to the water-required rules of different cotton growing periods, different irrigation strategies can be carried out automatically. This system had been used for precision irrigation of the cotton field in Langfang experimental farm of Soil and Fertilizer Institute, Chinese Academy of Agricultural Sciences in 2006. The results show that the fuzzy control irrigation system can improve cotton yield and save much water quantity than the irrigation system based on simple on-off control algorithm.

  20. A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

    NASA Technical Reports Server (NTRS)

    Hayashi, Isao; Naito, Eiichi; Ozawa, Jun; Wakami, Noboru

    1993-01-01

    A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function.

  1. Automated cloud classification with a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Tovinkere, Vasanth; Baum, Bryan A.

    1993-01-01

    An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

  2. Systems of fuzzy equations in structural mechanics

    NASA Astrophysics Data System (ADS)

    Skalna, Iwona; Rama Rao, M. V.; Pownuk, Andrzej

    2008-08-01

    Systems of linear and nonlinear equations with fuzzy parameters are relevant to many practical problems arising in structure mechanics, electrical engineering, finance, economics and physics. In this paper three methods for solving such equations are discussed: method for outer interval solution of systems of linear equations depending linearly on interval parameters, fuzzy finite element method proposed by Rama Rao and sensitivity analysis method. The performance and advantages of presented methods are described with illustrative examples. Extended version of the present paper can be downloaded from the web page of the UTEP [I. Skalna, M.V. Rama Rao, A. Pownuk, Systems of fuzzy equations in structural mechanics, The University of Texas at El Paso, Department of Mathematical Sciences Research Reports Series, , Texas Research Report No. 2007-01, 2007].

  3. 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.

  4. 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.

  5. Fuzzy Expert System to Characterize Students

    ERIC Educational Resources Information Center

    Van Hecke, T.

    2011-01-01

    Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…

  6. The Modeling of Fuzzy Systems Based on Lee-Oscillatory Chaotic Fuzzy Model (LoCFM)

    NASA Astrophysics Data System (ADS)

    Wong, Max H. Y.; Liu, James N. K.; Shum, Dennis T. F.; Lee, Raymond S. T.

    This paper introduces a new fuzzy membership function — LEE-oscillatory Chaotic Fuzzy Model (LoCFM). The development of this model is based on fuzzy logic and the incorporation of chaos theory — LEE Oscillator. Prototype systems are being developed for handling imprecise problems, typically involving linguistic expression and fuzzy semantic meaning. In addition, the paper also examines the mechanism of the LEE Oscillator through analyzing its structure and neural dynamics. It demonstrates the potential application of the model in future development.

  7. International Systems.

    ERIC Educational Resources Information Center

    Saba, Farhad, Ed.

    1999-01-01

    Completes a discussion of a systems model of distance education (in articles since May 1999) focusing on the most complex level, international. Discussion includes transfer of technology from United States universities to developing nations, the free market, and the age of the global economy. Presents a list of "early indicators" of changes in…

  8. Implementation of Fuzzy Inference Systems Using Neural Network Techniques

    DTIC Science & Technology

    1992-03-01

    rules required to implement the system, which are usually supplied by ’experts’. One alternative is to use a neural network -type architecture to implement...the fuzzy inference system, and neural network -type training techniques to ’learn’ the control parameters needed by the fuzzy inference system. By...using a generalized version of a neural network , the rules of the fuzzy inference system can be learned without the assistance of experts.

  9. Fuzzy systems in high-energy physics

    NASA Astrophysics Data System (ADS)

    Castellano, Marcello; Masulli, Francesco; Penna, Massimo

    1996-06-01

    Decision making is one of the major subjects of interest in physics. This is due to the intrinsic finite accuracy of measurement that leads to the possible results to span a region for each quantity. In this way, to recognize a particle type among the others by a measure of a feature vector, a decision must be made. The decision making process becomes a crucial point whenever a low statistical significance occurs as in space cosmic ray experiments where searching in rare events requires us to reject as many background events as possible (high purity), keeping as many signal events as possible (high efficiency). In the last few years, interesting theoretical results on some feedforward connectionist systems (FFCSs) have been obtained. In particular, it has been shown that multilayer perceptrons (MLPs), radial basis function networks (RBFs), and some fuzzy logic systems (FLSs) are nonlinear universal function approximators. This property permits us to build a system showing intelligent behavior , such as function estimation, time series forecasting, and pattern classification, and able to learn their skill from a set of numerical data. From the classification point of view, it has been demonstrated that non-parametric classifiers based FFCSs holding the universal function approximation property, can approximate the Bayes optimal discriminant function and then minimize the classification error. In this paper has been studied the FBF when applied to a high energy physics problem. The FBF is a powerful neuro-fuzzy system (or adaptive fuzzy logic system) holding the universal function approximation property and the capability of learning from examples. The FBF is based on product-inference rule (P), the Gaussian membership function (G), a singleton fuzzifier (S), and a center average defuzzifier (CA). The FBF can be regarded as a feedforward connectionist system with just one hidden layer whose units correspond to the fuzzy MIMO rules. The FBF can be identified both by

  10. Extending Fuzzy System Concepts for Control of a Vitrification Melter

    SciTech Connect

    Whitehouse, J.C.; Sorgel, W.; Garrison, A.; Schalkoff, R.J.

    1995-08-16

    Fuzzy systems provide a mathematical framework to capture uncertainty. The complete description of real, complex systems or situations often requires far more detail and information than could ever be obtained (or understood). Fuzzy approaches are an alternative technology for both system control and information processing and management. In this paper, we present the design of a fuzzy control system for a melter used in the vitrification of hazardous waste. Design issues, especially those related to melter shutdown and obtaining smooth control surfaces, are addressed. Several extensions to commonly-applied fuzzy techniques, notably adaptive defuzzification and modified rule structures are developed.

  11. Project DANA: multiagent simulation and fuzzy rules for international crisis detection--can we forestall wars?

    NASA Astrophysics Data System (ADS)

    Cozien, Roger F.; Colautti, Andre

    1999-11-01

    Assessing the conflicting potential of an international situation is very important in the exercise of Defence duty. Mastering a formal method allowing the detection of risky situations is a necessity. Our aim was to develop a highly operational method twinned with a computer simulation tool which can explore a huge number of potential war zones, and can test many hypotheses with high accuracy within reasonable time. We use a multi-agents system to describe an international situation. The agent coding allows us to give computer existence to very abstract concepts such as: a government, the economy, the armed forces, the foreign policy... We give to these agents fuzzy rules of behavior, those rules represent human expertise. In order to yardstick our model we used the Falklands war to make our first simulations. The main distortion between the historical reality and our simulations comes from our fuzzy controller which causes a great loss of information. We are going to change it to a more efficient one in order to fit the historical reality. Agent coding with fuzzy rules allows human experts to keep close to their statements and expertise, and they can handle this kind of tool quite easily.

  12. Fault detection in HVAC systems using fuzzy models

    NASA Astrophysics Data System (ADS)

    Dexter, A. L.; Mok, B. K. K.

    1993-12-01

    A fault detection scheme which uses qualitative models, consisting of sets of fuzzy rules, to describe the generic behavior of both fault free and faulty operation of plant is described. It is applied to Heating, Ventilating and Air Conditioning (HVAC) systems. These fuzzy reference models are generated off line from training data produced by computer simulation of a typical plant, with and without the faults, using a fuzzy identification scheme. A computationally efficient, fuzzy identification algorithm, that is suitable for implementation in packaged controls, is used to estimate the credibility of each of the rules. Faults are detected by comparing the behavior of the plant with the behavior predicted by the fuzzy reference models. Two methods of comparing the actual and predicted behavior are examined: a prediction based method in which faults are detected by comparing measurements, available from the building energy management system connected to the plant, with the predictions of the fuzzy reference models; and a rule similarity method in which data collected on line from the real plant are used to identify a partial fuzzy model. The degree to which faulty or correct operation is present, is determined by comparing the rules of the partial fuzzy model with the rules of the fuzzy reference models, using a fuzzy measure of similarity. Results which demonstrate the ability of both schemes to detect faults in the mixing box of the air handling unit of an air conditioning system are presented.

  13. A proposed method for solving fuzzy system of linear equations.

    PubMed

    Kargar, Reza; Allahviranloo, Tofigh; Rostami-Malkhalifeh, Mohsen; Jahanshaloo, Gholam Reza

    2014-01-01

    This paper proposes a new method for solving fuzzy system of linear equations with crisp coefficients matrix and fuzzy or interval right hand side. Some conditions for the existence of a fuzzy or interval solution of m × n linear system are derived and also a practical algorithm is introduced in detail. The method is based on linear programming problem. Finally the applicability of the proposed method is illustrated by some numerical examples.

  14. Finite solutions of fully fuzzy linear system

    NASA Astrophysics Data System (ADS)

    Malkawi, Ghassan; Ahmad, Nazihah; Ibrahim, Haslinda

    2014-12-01

    The solution of Fully Fuzzy Linear System (FFLS) is normally categorized as unique, finite and infinitely many solutions. However, in the case of more than one solution, the finite or alternative solution is not detected when linear programming is considered. Therefore this paper aims to provide a method of using min-max system and absolute system to append new concept for the consistency of FFLS, which is called finite solution of FFLS, where the FFLS have more than two solutions, and not only an infinite solution.

  15. Steady-state error of a system with fuzzy controller.

    PubMed

    Butkiewicz, B S

    1998-01-01

    We consider the problem of control error of a fuzzy system with feedback. The system consists of a plant, linear or nonlinear, fuzzy controller, and feedback loop. As controller we use both PD and PI fuzzy type controllers. We apply different t-norm and co-norm: logic, algebraic, Yager, Hamacher, bounded, drastic, etc. in the process of fuzzy reasoning. Triangular shape of membership functions is supposed, but we generalize the results obtained. Steady-state error of a system is calculated. We have obtained very interesting results. The steady-state error is identical for pairs of triangular t- and co-norms.

  16. Life insurance risk assessment using a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Carreno, Luis A.; Steel, Roy A.

    1992-01-01

    In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

  17. 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.

  18. 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.

  19. Application and classification of fuzzy dynamic system and fuzzy linguistic controller with examples illustrated

    NASA Astrophysics Data System (ADS)

    Wang, Paul P.; Tyan, Ching-Yu

    1993-12-01

    This paper presents the classification of fuzzy dynamic systems and fuzzy linguistic controllers (FLC) into standard types (TYPE 1 through TYPE 7). The need, utility value, and the logic behind this classification are given. The proposed classification is the result of studying many known examples of FLC applications. The impact of this classification to new designs and to the improved performance of classical and modern control systems is an important consideration.

  20. Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems.

    PubMed

    Kumar, Abhishek; Sharma, Rajneesh

    2017-03-01

    We propose a fuzzy reinforcement learning (RL) based controller that generates a stable control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at lyapunov constraining the consequent part of fuzzy rules in a fuzzy RL setup. Ours is a first attempt at designing a linguistic RL controller with lyapunov constrained fuzzy consequents to progressively learn a stable optimal policy. The proposed controller does not need system model or desired response and can effectively handle disturbances in continuous state-action space problems. Proposed controller has been employed on the benchmark Inverted Pendulum (IP) and Rotational/Translational Proof-Mass Actuator (RTAC) control problems (with and without disturbances). Simulation results and comparison against a) baseline fuzzy Q learning, b) Lyapunov theory based Actor-Critic, and c) Lyapunov theory based Markov game controller, elucidate stability and viability of the proposed control scheme.

  1. 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.

  2. 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.

  3. A New Fuzzy System Based on Rectangular Pyramid

    PubMed Central

    Jiang, Mingzuo; Yuan, Xuehai; Li, Hongxing; Wang, Jiaxia

    2015-01-01

    A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation. PMID:25874253

  4. Fuzzy modeling for chaotic systems via interval type-2 T-S fuzzy model with parametric uncertainty

    NASA Astrophysics Data System (ADS)

    Hasanifard, Goran; Gharaveisi, Ali Akbar; Vali, Mohammad Ali

    2014-02-01

    A motivation for using fuzzy systems stems in part from the fact that they are particularly suitable for processes when the physical systems or qualitative criteria are too complex to model and they have provided an efficient and effective way in the control of complex uncertain nonlinear systems. To realize a fuzzy model-based design for chaotic systems, it is mostly preferred to represent them by T-S fuzzy models. In this paper, a new fuzzy modeling method has been introduced for chaotic systems via the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. An IT2 fuzzy model is proposed to represent a chaotic system subjected to parametric uncertainty, covered by the lower and upper membership functions of the interval type-2 fuzzy sets. Investigating many well-known chaotic systems, it is obvious that nonlinear terms have a single common variable or they depend only on one variable. If it is taken as the premise variable of fuzzy rules and another premise variable is defined subject to parametric uncertainties, a simple IT2 T-S fuzzy dynamical model can be obtained and will represent many well-known chaotic systems. This IT2 T-S fuzzy model can be used for physical application, chaotic synchronization, etc. The proposed approach is numerically applied to the well-known Lorenz system and Rossler system in MATLAB environment.

  5. A survey on fuzzy theory applied in geographic information system

    NASA Astrophysics Data System (ADS)

    Wu, Feiquan; Cui, Weihong; Chen, Houwu

    2009-10-01

    The real world is an infinite complex and very huge systems, the phenomenon and processes in this world have many complex relations among them. It is consecutive and cannot be treated as a determined one as the traditional geographic information system does; What's more, the uncertainty and fuzziness exist in every stage of data processing of GIS, from data collection, data storage to data analysis etc, so it is very meaningful to apply fuzzy theory in GIS for its ability to handle fuzziness and uncertainty of spatial data. The paper talks about the current situation of fuzzy theory applied in GIS, including the classification of application fields, its main methods, principles etc. The detailed fields we concerned include spatial object modeling, spatial reasoning, spatial analysis, spatial data mining, and reliability analysis of GIS data and so on. Furthermore, we put forward some development foregrounds and research orientations of fuzzy theory applied in GIS.

  6. Fuzzy expert system for diagnosing diabetic neuropathy

    PubMed Central

    Rahmani Katigari, Meysam; Ayatollahi, Haleh; Malek, Mojtaba; Kamkar Haghighi, Mehran

    2017-01-01

    AIM To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy. METHODS The research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists’ perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system. RESULTS The final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%). CONCLUSION The system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients. PMID:28265346

  7. Evaluation of Fuzzy Rulemaking for Expert Systems for Failure Detection

    NASA Technical Reports Server (NTRS)

    Laritz, F.; Sheridan, T. B.

    1984-01-01

    Computer aids in expert systems were proposed to diagnose failures in complex systems. It is shown that the fuzzy set theory of Zadeh offers a new perspective for modeling for humans thinking and language use. It is assumed that real expert human operators of aircraft, power plants and other systems do not think of their control tasks or failure diagnosis tasks in terms of control laws in differential equation form, but rather keep in mind a set of rules of thumb in fuzzy form. Fuzzy set experiments are described.

  8. Fuzzy system applications for short-term electric load forecasting

    NASA Astrophysics Data System (ADS)

    Al-Kandari, Ahmad Mohammad

    Load forecasting is an important function in economic power generation, allocation between plants (Unit Commitment Scheduling), maintenance scheduling, and for system security applications such as peak shaving by power interchange with interconnected utilities. In this thesis the problem of fuzzy short term load forecasting is formulated and solved. The thesis starts with a discussion of conventional algorithms used in short-term load forecasting. These algorithms are based on least error squares and least absolute value. The theory behind each algorithm is explained. Three different models are developed and tested in the first part of the thesis. The first model (A) is a regression model that takes into account the weather parameters in summer and winter seasons. The second model (B) is a harmonics based model, which does not account for weather parameters, but considers the parameters as a function of time. Model (B) can be used where variations in weather parameters are not available. Finally, model (C) is created as a hybrid combination of models A and B. The parameters of the three models are estimated using the two static estimation algorithms and are used later to predict the load for twenty-four hours ahead. The results obtained are discussed and conclusions are drawn for these models. In the second part of the thesis new fuzzy models are developed for crisp load power with fuzzy load parameters and for fuzzy load power with fuzzy load parameters. Three fuzzy models (A), (B) and (C) are developed. The fuzzy load model (A) is a fuzzy linear regression model for summer and winter seasons. Model (B) is a harmonic fuzzy model, which does not account for weather parameters. Finally fuzzy load model (C) is a hybrid combination of fuzzy load models (A) and (B). Estimating the fuzzy parameters for the three models turns out to be one of linear optimization. The fuzzy parameters are obtained for the three models. These parameters are used to predict the load as a

  9. Adaptive fuzzy logic control of a static VAR system

    SciTech Connect

    Dash, P.K.; Routray, A.; Panda, P.C.; Panda, S.K.

    1995-12-31

    A fuzzy gain scheduling scheme for PID controller for transient and dynamic voltage stabilization of power transmission systems has been presented in this paper. Fuzzy rules and reasoning are utilized on-line to determine the controller parameters based on the error signal and its derivative. The static VAR controller is designed with the bus angle deviation and its rate as the input signal to a fuzzy PI or PID control loop. This control is tested for a power transmission system supplying dynamic loads and provides superior performance.

  10. Applications of fuzzy theories to multi-objective system optimization

    NASA Technical Reports Server (NTRS)

    Rao, S. S.; Dhingra, A. K.

    1991-01-01

    Most of the computer aided design techniques developed so far deal with the optimization of a single objective function over the feasible design space. However, there often exist several engineering design problems which require a simultaneous consideration of several objective functions. This work presents several techniques of multiobjective optimization. In addition, a new formulation, based on fuzzy theories, is also introduced for the solution of multiobjective system optimization problems. The fuzzy formulation is useful in dealing with systems which are described imprecisely using fuzzy terms such as, 'sufficiently large', 'very strong', or 'satisfactory'. The proposed theory translates the imprecise linguistic statements and multiple objectives into equivalent crisp mathematical statements using fuzzy logic. The effectiveness of all the methodologies and theories presented is illustrated by formulating and solving two different engineering design problems. The first one involves the flight trajectory optimization and the main rotor design of helicopters. The second one is concerned with the integrated kinematic-dynamic synthesis of planar mechanisms. The use and effectiveness of nonlinear membership functions in fuzzy formulation is also demonstrated. The numerical results indicate that the fuzzy formulation could yield results which are qualitatively different from those provided by the crisp formulation. It is felt that the fuzzy formulation will handle real life design problems on a more rational basis.

  11. Robust support vector machine-trained fuzzy system.

    PubMed

    Forghani, Yahya; Yazdi, Hadi Sadoghi

    2014-02-01

    Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.

  12. Mamdani Fuzzy System for Indoor Autonomous Mobile Robot

    NASA Astrophysics Data System (ADS)

    Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.

    2011-06-01

    Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.

  13. Fuzzy logic applications to expert systems and control

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Jani, Yashvant

    1991-01-01

    A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.

  14. An Automatic KANSEI Fuzzy Rule Creating System Using Thesaurus

    NASA Astrophysics Data System (ADS)

    Hotta, Hajime; Hagiwara, Masafumi

    In this paper, we propose an automatic Kansei fuzzy rule creating system using thesaurus. In general, there are a lot of words that express impressions. However, conventional approaches of Kansei engineering are not suitable to use many impression words because it is difficult to collect enough data. The proposed system is an enhanced algorithm of the conventional method that the authors proposed before. The proposed system extracts fuzzy rules for many words defined in the thesaurus dictionary while the conventional one can extract rules of specified words which user defined. The flow of the system consists of 3 steps: (1) construction of thesaurus networks; (2) data collection by web questionnaire sheets; (3) Extraction of fuzzy rules. In order to extract Kansei fuzzy rules, the system employs enhanced GRNN(general regression neural network) which can treat relative words of the thesaurus network. Using a Japanese thesaurus dictionary in the experiments, the sets of fuzzy rules for 1,195 impression words are extracted, and the fuzzy rules extracted by the proposed system obtained higher accuracy than those extracted by the conventional one.

  15. Fuzzy logic controllers: A knowledge-based system perspective

    NASA Technical Reports Server (NTRS)

    Bonissone, Piero P.

    1993-01-01

    Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.

  16. Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

    NASA Technical Reports Server (NTRS)

    Culbert, Christopher J. (Editor)

    1993-01-01

    Documented here are 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. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. 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.

  17. 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.

  18. Fuzzy Adaptive Control System of a Non-Stationary Plant

    NASA Astrophysics Data System (ADS)

    Nadezhdin, Igor S.; Goryunov, Alexey G.; Manenti, Flavio

    2016-08-01

    This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

  19. Novel Fuzzy Modeling and Synchronization of Chaotic Systems With Multinonlinear Terms by Advanced Ge-Li Fuzzy Model.

    PubMed

    Li, Shih-Yu; Tam, Lap-Mou; Tsai, Shang-En; Ge, Zheng-Ming

    2015-09-11

    Ge and Li proposed an alternative strategy to model and synchronize two totally different nonlinear systems in the end of 2011, which provided a new version for fuzzy modeling and has been applied to several fields to simplify their modeling works and solve the mismatch problems [1]-[17]. However, the proposed model limits the number of nonlinear terms in each equation so that this model could not be used in all kinds of nonlinear dynamic systems. As a result, in this paper, a more efficient and comprehensive advanced-Ge-Li fuzzy model is given to further release the limitation and improve the effectiveness of the original one. The novel fuzzy model can be applied to all kinds of complex nonlinear systems--this is the universal strategy and only m x 2 fuzzy rules as well as two linear subsystems are needed to simulate nonlinear behaviors (m is the number of states in a nonlinear dynamic system), whatever the nonlinear terms are copious or complicated. Further, the fuzzy synchronization of two nonlinear dynamic systems with totally distinct structures can be achieved via only two sets of control gains designed through the novel fuzzy model as well as its corresponding fuzzy synchronization scheme. Two complicated dynamic systems are designed to be the illustrations, Mathieu-Van der pol system with uncertainties and Quantum-cellular neural networks nano system with uncertainties, to show the effectiveness and feasibility of the novel fuzzy model.

  20. Fuzzy Logic: A New Tool for the Analysis and Organization of International Business Communications.

    ERIC Educational Resources Information Center

    Sondak, Norman E.; Sondak, Eileen M.

    Classical western logic, built on a foundation of true/false, yes/no, right/wrong statements, leads to many difficulties and inconsistencies in the logical analysis and organization of international business communications. This paper presents the basic principles of classical logic and of fuzzy logic, a type of logic developed to allow for…

  1. H(infinity) filtering for fuzzy singularly perturbed systems.

    PubMed

    Yang, Guang-Hong; Dong, Jiuxiang

    2008-10-01

    This paper considers the problem of designing H(infinity) filters for fuzzy singularly perturbed systems with the consideration of improving the bound of singular-perturbation parameter epsilon. First, a linear-matrix-inequality (LMI)-based approach is presented for simultaneously designing the bound of the singularly perturbed parameter epsilon, and H(infinity) filters for a fuzzy singularly perturbed system. When the bound of singularly perturbed parameter epsilon is not under consideration, the result reduces to an LMI-based design method for H(infinity) filtering of fuzzy singularly perturbed systems. Furthermore, a method is given for evaluating the upper bound of singularly perturbed parameter subject to the constraint that the considered system is to be with a prescribed H(infinity) performance bound, and the upper bound can be obtained by solving a generalized eigenvalue problem. Finally, numerical examples are given to illustrate the effectiveness of the proposed methods.

  2. Fuzzy logic-based spike sorting system.

    PubMed

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2011-05-15

    We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a 'spikiness index' from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

  3. Control synthesis of continuous-time T-S fuzzy systems with local nonlinear models.

    PubMed

    Dong, Jiuxiang; Wang, Youyi; Yang, Guang-Hong

    2009-10-01

    This paper is concerned with the problem of designing fuzzy controllers for a class of nonlinear dynamic systems. The considered nonlinear systems are described by T-S fuzzy models with nonlinear local models, and the fuzzy models have fewer fuzzy rules than conventional T-S fuzzy models with local linear models. A new fuzzy control scheme with local nonlinear feedbacks is proposed, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). In contrast to the existing methods for fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Moreover, the local nonlinear feedback laws in the new fuzzy controllers are also helpful in achieving good control effects. Numerical examples are given to illustrate the effectiveness of the proposed method.

  4. Fuzzy Systems Modeling of In Situ Bioremediation of Chlorinated Solvents

    NASA Astrophysics Data System (ADS)

    Faybishenko, B.; Hazen, T. C.

    2001-12-01

    A large-scale vadose zone-groundwater bioremediation demonstration was conducted at the Savannah River Site (SRS) by injecting several types of gases (ambient air, methane, and nitrous oxide and triethyl phosphate mixtures) through a horizontal well in the groundwater at a 175 ft depth. Simultaneously, soil gas was extracted through a parallel horizontal well in the vadose zone at a 80 ft depth Monitoring revealed a wide range of spatial and temporal variations of concentrations of VOCs, enzymes, and biomass in groundwater and vadose zone monitoring boreholes over the field site. One of the powerful modern approaches to analyze uncertain and imprecise data chemical data is based on the use of methods of fuzzy systems modeling. Using fuzzy modeling we analyzed the spatio-temporal TCE and PCE concentrations and methanotroph densities in groundwater to assess the effectiveness of different campaigns of air stripping and bioremediation, and to determine the fuzzy relationship between these compounds. Our analysis revealed some details about the processes involved in remediation, which were not identified in the previous studies of the SRS demonstration. We also identified some future directions for using fuzzy systems modeling, such as the evaluation of the mass balance of the vadose zone - groundwater system, and the development of fuzzy-ruled methods for optimization of managing remediation activities, predictions, and risk assessment.

  5. Neuro-Fuzzy Control for Pneumatic Servo System

    NASA Astrophysics Data System (ADS)

    Shibata, Satoru; Jindai, Mitsuru; Yamamoto, Tomonori; Shimizu, Akira

    A learning method for acquiring the appropriate fuzzy rules using error back propagation to improve the control performance of the pneumatic servo system is presented in this paper. In the proposed method, two criteria are defined and are adjusted so as to minimize them using error back propagation. These criteria are defined on the fuzzy rules, that is, shapes of membership functions of antecedent clause and real values of consequent clause in the fuzzy controller. Two differentiating coefficients of the plant, used in error back propagation with respect to those criteria, are estimated by the newly established neural network. Moreover, sigmoid function is introduced for the connection of the neural network to compensate for the effect of non-linearity of the system. The method was applied to an existent vertical type pneumatic servo system and proved its effectiveness for practical use.

  6. Xrays: A fuzzy expert system for qualitative XRD analysis

    NASA Astrophysics Data System (ADS)

    Wright, D.; Liu, C. L.; Stanley, D.; Chen, H. C.; Fang, J. H.

    1993-11-01

    This paper describes the development of a fuzzy expert system termed XRAYS for identification of minerals via X-ray diffractograms. The system emulates the well-known (manual) Hanawalt method, thus avoiding the black-box approach of most computer search/match programs. The mineral subfile of the JCPDS Powder Diffraction file is stored in a database, from which the Hanawalt groups are created by the program. The expert system then carries out "manual" search following the steps prescribed for the Hanawalt method. Fuzzy comparisons and fuzzy arithmetic operations are employed in searching for matches. A list of candidate minerals is output in decreasing order of confidence. Graphical comparisons between the unknown pattern and candidate patterns are displayed on the screen to allow the diffractionists to make visual comparison as to the degree of match. Several examples containing from two to six minerals are used for illustration.

  7. Sinusoidal rotatory chair system by an auto-tuning fuzzy PID controller

    SciTech Connect

    Park, H.A.; Cha, I.S.; Baek, H.L.

    1995-12-31

    This paper presents DC servo motor speed control characteristics by fuzzy logic controller and considers position following control response with controller. A sinusoidal rotatory chair system using an auto tuning fuzzy PID control was designed to evaluate the vestibular function. Then the system is investigated for the effects of change by the fuzziness of fuzzy variable. If this system is supported by a channel, it is considered for application in industry of multi joint robot and precision parallel driving.

  8. Application of genetic algorithms to tuning fuzzy control systems

    NASA Technical Reports Server (NTRS)

    Espy, Todd; Vombrack, Endre; Aldridge, Jack

    1993-01-01

    Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

  9. Fuzzy state/disturbance observer design for T-S fuzzy systems with application to sensor fault estimation.

    PubMed

    Gao, Zhiwei; Shi, Xiaoyan; Ding, Steven X

    2008-06-01

    A novel fuzzy-observer-design approach is presented for Takagi-Sugeno fuzzy models with unknown output disturbances. In order to decouple the unknown output disturbance, an augmented fuzzy descriptor model is constructed by supposing the disturbance to be an auxiliary state vector. A fuzzy state-space observer is next designed for the augmented fuzzy descriptor system, and the simultaneous estimates of the original state and disturbance are thus obtained. The proposed observer technique is further applied to estimate sensor faults. Finally, a numerical example is given to illustrate the design procedure, and the simulation results show the desired tracking performance. The preknowledge of the disturbance and fault is not necessary for our design. Moreover, the considered disturbance and sensor fault can be in any form.

  10. Workshop on Fuzzy Control Systems and Space Station Applications

    NASA Technical Reports Server (NTRS)

    Aisawa, E. K. (Compiler); Faltisco, R. M. (Compiler)

    1990-01-01

    The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented.

  11. Fuzzy stochastic neural network model for structural system identification

    NASA Astrophysics Data System (ADS)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  12. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

    PubMed

    Kuo, R J; Wu, P; Wang, C P

    2002-09-01

    Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

  13. Fuzzy self-learning control for magnetic servo system

    NASA Technical Reports Server (NTRS)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  14. 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

  15. Prediction of Earth rotation parameters by fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Akyilmaz, O.; Kutterer, H.

    2004-09-01

    The short-term prediction of Earth rotation parameters (ERP) (length-of-day and polar motion) is studied up to 10 days by means of ANFIS (adaptive network based fuzzy inference system). The prediction is then extended to 40 days into the future by using the formerly predicted values as input data. The ERP C04 time series with daily values from the International Earth Rotation Service (IERS) serve as the data base. Well-known effects in the ERP series, such as the impact of the tides of the solid Earth and the oceans or seasonal variations of the atmosphere, were removed a priori from the C04 series. The residual series were used for both training and validation of the network. Different network architectures are discussed and compared in order to optimize the network solution. The results of the prediction are analyzed and compared with those of other methods. Short-term ERP values predicted by ANFIS show root-mean-square errors which are equal to or even lower than those from the other considered methods. The presented method is easy to use.

  16. Seizure detection in intracranial EEG using a fuzzy inference system.

    PubMed

    Aarabi, A; Fazel-Rezai, R; Aghakhani, Y

    2009-01-01

    In this paper, we present a fuzzy rule-based system for the automatic detection of seizures in the intracranial EEG (IEEG) recordings. A total of 302.7 hours of the IEEG with 78 seizures, recorded from 21 patients aged between 10 and 47 years were used for the evaluation of the system. After preprocessing, temporal, spectral, and complexity features were extracted from the segmented IEEGs. The results were thresholded using the statistics of a reference window and integrated spatio-temporally using a fuzzy rule-based decision making system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11 s. The results from the automatic system correlate well with the visual analysis of the seizures by the expert. This system may serve as a good seizure detection tool for monitoring long-term IEEG with relatively high sensitivity and low false detection rate.

  17. An intelligent robotic system based on a fuzzy approach

    SciTech Connect

    Fukuda, Toshio; Kubota, Naoyuki

    1999-09-01

    This paper deals with a fuzzy-based intelligent robotic system that requires various capabilities normally associated with intelligence. It acquires skills and knowledge through interaction with a dynamic environment. Recently, subsumption architectures, behavior-based artificial intelligence, and behavioral engineering for robotic systems have been discussed as new technologies for intelligent robotic systems. This paper proposes a robotic system with structured intelligence. The authors focus on a mobile robotic system with a fuzzy controller and propose a sensory network that allows the robot to perceive its environment. An evolutionary approach improves the robot's performance. Furthermore, the authors discuss the effectiveness of the proposed method through computer simulations of collision avoidance and path-planning problems.

  18. Use of Fuzzy Logic Systems for Assessment of Primary Faults

    NASA Astrophysics Data System (ADS)

    Petrović, Ivica; Jozsa, Lajos; Baus, Zoran

    2015-09-01

    In electric power systems, grid elements are often subjected to very complex and demanding disturbances or dangerous operating conditions. Determining initial fault or cause of those states is a difficult task. When fault occurs, often it is an imperative to disconnect affected grid element from the grid. This paper contains an overview of possibilities for using fuzzy logic in an assessment of primary faults in the transmission grid. The tool for this task is SCADA system, which is based on information of currents, voltages, events of protection devices and status of circuit breakers in the grid. The function model described with the membership function and fuzzy logic systems will be presented in the paper. For input data, diagnostics system uses information of protection devices tripping, states of circuit breakers and measurements of currents and voltages before and after faults.

  19. 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.

  20. Intelligent micro blood typing system using a fuzzy algorithm

    NASA Astrophysics Data System (ADS)

    Kang, Taeyun; Lee, Seung-Jae; Kim, Yonggoo; Lee, Gyoo-Whung; Cho, Dong-Woo

    2010-01-01

    ABO typing is the first analysis performed on blood when it is tested for transfusion purposes. The automated machines used in hospitals for this purpose are typically very large and the process is complicated. In this paper, we present a new micro blood typing system that is an improved version of our previous system (Kang et al 2004 Trans. ASME, J. Manuf. Sci. Eng. 126 766, Lee et al 2005 Sensors Mater. 17 113). This system, fabricated using microstereolithography, has a passive valve for controlling the flow of blood and antibodies. The intelligent micro blood typing system has two parts: a single-line micro blood typing device and a fuzzy expert system for grading the strength of agglutination. The passive valve in the single-line micro blood typing device makes the blood stop at the entrance of a micro mixer and lets it flow again after the blood encounters antibodies. Blood and antibodies are mixed in the micro mixer and agglutination occurs in the chamber. The fuzzy expert system then determines the degree of agglutination from images of the agglutinated blood. Blood typing experiments using this device were successful, and the fuzzy expert system produces a grading decision comparable to that produced by an expert conducting a manual analysis.

  1. Fuzzy backward reasoning using fuzzy Petri nets.

    PubMed

    Chen, S M

    2000-01-01

    Chen, Ke and Chang (1990) have presented a fuzzy forward reasoning algorithm for rule-based systems using fuzzy Petri nets. In this paper, we extend the work of Chen, Ke and Chang (1990) to present a fuzzy backward reasoning algorithm for rule-based systems using fuzzy Petri nets, where the fuzzy production rules of a rule-based system are represented by fuzzy Petri nets. The system can perform fuzzy backward reasoning automatically to evaluate the degree of truth of any proposition specified by the user. The fuzzy backward reasoning capability allows the computers to perform reasoning in a more flexible manner and to think more like people.

  2. The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

    NASA Astrophysics Data System (ADS)

    Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti

    2014-06-01

    In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

  3. 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.

  4. Skin Cancer Recognition by Using a Neuro-Fuzzy System

    PubMed Central

    Salah, Bareqa; Alshraideh, Mohammad; Beidas, Rasha; Hayajneh, Ferial

    2011-01-01

    Skin cancer is the most prevalent cancer in the light-skinned population and it is generally caused by exposure to ultraviolet light. Early detection of skin cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer. The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type. The sensitivity of NN in diagnosing skin cancer was 95%, while the specificity was 88%. Skin cancer diagnosis by neuro-fuzzy system achieved sensitivity of 98% and a specificity of 89%. PMID:21340020

  5. Document Retrieval Using A Fuzzy Knowledge-Based System

    NASA Astrophysics Data System (ADS)

    Subramanian, Viswanath; Biswas, Gautam; Bezdek, James C.

    1986-03-01

    This paper presents the design and development of a prototype document retrieval system using a knowledge-based systems approach. Both the domain-specific knowledge base and the inferencing schemes are based on a fuzzy set theoretic framework. A query in natural language represents a request to retrieve a relevant subset of documents from a document base. Such a query, which can include both fuzzy terms and fuzzy relational operators, is converted into an unambiguous intermediate form by a natural language interface. Concepts that describe domain topics and the relationships between concepts, such as the synonym relation and the implication relation between a general concept and more specific concepts, have been captured in a knowledge base. The knowledge base enables the system to emulate the reasoning process followed by an expert, such as a librarian, in understanding and reformulating user queries. The retrieval mechanism processes the query in two steps. First it produces a pruned list of documents pertinent to the query. Second, it uses an evidence combination scheme to compute a degree of support between the query and individual documents produced in step one. The front-end component of the system then presents a set of document citations to the user in ranked order as an answer to the information request.

  6. 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.

  7. A fuzzy inference system to evaluate contract service provider performance.

    PubMed

    Cruz, Antonio Miguel; Denis, Ernesto Rodriguez

    2005-01-01

    This paper puts forward a fuzzy inference system for evaluating the quality performance of service contract providers. An Application Service Provider was designed and put online, featuring surveys to establish the most useful indicators to evaluate the quality of the service. This model was implemented in 10 separate hospitals. As a result, the service cost-acquisition cost ratio in these cases was reduced from 16.14% to 6.09% in the period 2001-January 2003.

  8. Assessing System Architectures: The Canonical Decomposition Fuzzy Comparative Methodology

    DTIC Science & Technology

    2011-01-01

    important characteristic of fuzzy set theory is that it supports partial membership. If the membership function for an element in A is valid for the...Verification – the distinguishing characteristic of verification is a traceability analysis to determine whether all the allocated functionality and constraints...performance in a manner that is tolerant of ambiguity. Ambiguity is an inherent characteristic of systems architecting and must therefore be

  9. Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.

    PubMed

    Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim

    2014-09-19

    Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds  and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.

  10. Fuzzy assessment of health information system users' security awareness.

    PubMed

    Aydın, Özlem Müge; Chouseinoglou, Oumout

    2013-12-01

    Health information systems (HIS) are a specific area of information systems (IS), where critical patient data is stored and quality health service is only realized with the correct use and efficient dissemination of this data to health workers. Therefore, a balance needs to be established between the levels of security and flow of information on HIS. Instead of implementing higher levels and further mechanisms of control to increase the security of HIS, it is preferable to deal with the arguably weakest link on HIS chain with respect to security: HIS users. In order to provide solutions and approaches for transforming users to the first line of defense in HIS but also to employ capable and appropriate candidates from the pool of newly graduated students, it is important to assess and evaluate the security awareness levels and characteristics of these existing and future users. This study aims to provide a new perspective to understand the phenomenon of security awareness of HIS users with the use of fuzzy analysis, and to assess the present situation of current and future HIS users of a leading medical and educational institution of Turkey, with respect to their security characteristics based on four different security scales. The results of the fuzzy analysis, the guide on how to implement this fuzzy analysis to any health institution and how to read and interpret these results, together with the possible implications of these results to the organization are provided.

  11. Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

    NASA Astrophysics Data System (ADS)

    Gering, Stefan; Adamy, Jürgen

    2014-12-01

    Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.

  12. Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering

    NASA Astrophysics Data System (ADS)

    Panomruttanarug, Benjamas; Higuchi, Kohji

    This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.

  13. Robust adaptive self-structuring fuzzy control design for nonaffine, nonlinear systems

    NASA Astrophysics Data System (ADS)

    Chen, Pin-Cheng; Wang, Chi-Hsu; Lee, Tsu-Tian

    2011-01-01

    In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L 2 tracking performance to stabilise the closed-loop system. This L 2 tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.

  14. Delay-dependent fuzzy static output feedback control for discrete-time fuzzy stochastic systems with distributed time-varying delays.

    PubMed

    Xia, ZhiLe; Li, JunMin; Li, JiangRong

    2012-11-01

    This paper is concerned with the delay-dependent H(∞) fuzzy static output feedback control scheme for discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems with distributed time-varying delays. To begin with, the T-S fuzzy stochastic system is transformed to an equivalent switching fuzzy stochastic system. Then, based on novel matrix decoupling technique, improved free-weighting matrix technique and piecewise Lyapunov-Krasovskii function (PLKF), a new delay-dependent H(∞) fuzzy static output feedback controller design approach is first derived for the switching fuzzy stochastic system. Some drawbacks existing in the previous papers such as matrix equalities constraint, coordinate transformation, the same output matrices, diagonal structure constraint on Lyapunov matrices and BMI problem have been eliminated. Since only a set of LMIs is involved, the controller parameters can be solved directly by the Matlab LMI toolbox. Finally, two examples are provided to illustrate the validity of the proposed method.

  15. Fuzzy control system for a remote focusing microscope

    NASA Technical Reports Server (NTRS)

    Weiss, Jonathan J.; Tran, Luc P.

    1992-01-01

    Space Station Crew Health Care System procedures require the use of an on-board microscope whose slide images will be transmitted for analysis by ground-based microbiologists. Focusing of microscope slides is low on the list of crew priorities, so NASA is investigating the option of telerobotic focusing controlled by the microbiologist on the ground, using continuous video feedback. However, even at Space Station distances, the transmission time lag may disrupt the focusing process, severely limiting the number of slides that can be analyzed within a given bandwidth allocation. Substantial time could be saved if on-board automation could pre-focus each slide before transmission. The authors demonstrate the feasibility of on-board automatic focusing using a fuzzy logic ruled-based system to bring the slide image into focus. The original prototype system was produced in under two months and at low cost. Slide images are captured by a video camera, then digitized by gray-scale value. A software function calculates an index of 'sharpness' based on gray-scale contrasts. The fuzzy logic rule-based system uses feedback to set the microscope's focusing control in an attempt to maximize sharpness. The systems as currently implemented performs satisfactorily in focusing a variety of slide types at magnification levels ranging from 10 to 1000x. Although feasibility has been demonstrated, the system's performance and usability could be improved substantially in four ways: by upgrading the quality and resolution of the video imaging system (including the use of full color); by empirically defining and calibrating the index of image sharpness; by letting the overall focusing strategy vary depending on user-specified parameters; and by fine-tuning the fuzzy rules, set definitions, and procedures used.

  16. An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

    PubMed

    Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

    2015-07-01

    We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

  17. Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment.

    PubMed

    Tatari, Farzaneh; Akbarzadeh-T, Mohammad-R; Sabahi, Ahmad

    2012-12-01

    In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1 year. The results are then compared with a fuzzy distributed approach.

  18. A Novel Approach to Modeling of Hydrogeologic Systems Using Fuzzy Differential Equations

    NASA Astrophysics Data System (ADS)

    Faybishenko, B. A.

    2003-12-01

    The many simultaneously occurring processes in unsaturated-saturated heterogeneous soils and fractured rocks can cause field observations to become imprecise and incomplete. Consequently, the results of predictions using deterministic and stochastic mathematical models are often uncertain, vague or "fuzzy." One of the alternative approaches to modeling hydrogeologic systems is the application of a fuzzy-systems approach, which is already widely used in such fields as engineering, physics, chemistry, and biology. After presenting a hydrogeologic system as a fuzzy system, the author presents a fuzzy form of Darcy's equation. Based on this equation, second-order fuzzy partial differential equations of the elliptic type (analogous to the Laplace equation) and the parabolic type (analogous to the Richards equation) are derived. These equations are then approximated as fuzzy-difference equations and solved using the basic principles of fuzzy arithmetic. The solutions for the fuzzy-difference equations take the form of fuzzy membership functions for each observation point (node). The author gives examples of the solutions of these equations for flow in unsaturated and saturated media and then compares them with those obtained using deterministic and stochastic methods.

  19. A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System

    PubMed Central

    Tang, Yongchuan; Zhou, Deyun

    2016-01-01

    In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method. PMID:27482707

  20. A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System.

    PubMed

    Tang, Yongchuan; Zhou, Deyun; Jiang, Wen

    2016-01-01

    In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method.

  1. 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.

  2. 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

  3. Developing a Software for Fuzzy Group Decision Support System: A Case Study

    ERIC Educational Resources Information Center

    Baba, A. Fevzi; Kuscu, Dincer; Han, Kerem

    2009-01-01

    The complex nature and uncertain information in social problems required the emergence of fuzzy decision support systems in social areas. In this paper, we developed user-friendly Fuzzy Group Decision Support Systems (FGDSS) software. The software can be used for multi-purpose decision making processes. It helps the users determine the main and…

  4. 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

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

    PubMed

    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.

  6. Error Correction, Control Systems and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Smith, Earl B.

    2004-01-01

    This paper will be a discussion on dealing with errors. While error correction and communication is important when dealing with spacecraft vehicles, the issue of control system design is also important. There will be certain commands that one wants a motion device to execute. An adequate control system will be necessary to make sure that the instruments and devices will receive the necessary commands. As it will be discussed later, the actual value will not always be equal to the intended or desired value. Hence, an adequate controller will be necessary so that the gap between the two values will be closed.

  7. A Simple and Effective Remedial Learning System with a Fuzzy Expert System

    ERIC Educational Resources Information Center

    Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.

    2016-01-01

    This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…

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

    PubMed

    Wang, Wei; Tong, Shaocheng

    2017-01-10

    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.

  9. The International System.

    ERIC Educational Resources Information Center

    East, Maurice A.

    Designed as a unit for an international relations course, this systems approach paper outlines a learning method which contributes to the student's awareness that the United States is only one of many actors in the world. It also makes the student aware that there are limitations on the U. S. individual actions because of this interdependence and…

  10. Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

    NASA Astrophysics Data System (ADS)

    Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

    2017-02-01

    In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

  11. H(∞) constrained fuzzy control via state observer feedback for discrete-time Takagi-Sugeno fuzzy systems with multiplicative noises.

    PubMed

    Chang, Wen-Jer; Wu, Wen-Yuan; Ku, Cheung-Chieh

    2011-01-01

    The purpose of this paper is to study the H(∞) constrained fuzzy controller design problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with multiplicative noises by using the state observer feedback technique. The proposed fuzzy controller design approach is developed based on the Parallel Distributed Compensation (PDC) technique. Through the Lyapunov stability criterion, the stability analysis is completed to develop stability conditions for the closed-loop systems. Besides, the H(∞) performance constraints is also considered in the stability condition derivations for the worst case effect of disturbance on system states. Solving these stability conditions via the two-step Linear Matrix Inequality (LMI) algorithm, the observer-based fuzzy controller is obtained to achieve the stability and H(∞) performance constraints, simultaneously. Finally, a numerical example is provided to verify the applicability and effectiveness of the proposed fuzzy control approach.

  12. Reducing the Impact of Uncertainties in Networked Control Systems Using Type-2 Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Michal, Blaho; J´n, Murgaš; Eugen, Viszus; Peter, Fodrek

    2015-01-01

    The networked control systems (NCS) have grown in popularity in recent years. Despite their advantages over the traditional control schemes, some of their drawbacks emerged as well (time delays, packet losses). There are several ways of dealing with the time delays and packet losses in NCS, but only a few authors have ever used type-2 fuzzy controllers for this purpose to our knowledge. This paper is aimed at dealing with the negative effects that occur in NCS, by using type-2 fuzzy control systems. It is presented that this approach can be successfully used to decrease the effects of time delays and packet losses. A type-2 fuzzy controller has been designed and compared to a type-1 fuzzy controller. The intervals of type-2 fuzzy controller were optimized via genetic algorithm.

  13. Assessing water quality in rivers with fuzzy inference systems: a case study.

    PubMed

    Ocampo-Duque, William; Ferré-Huguet, Núria; Domingo, José L; Schuhmacher, Marta

    2006-08-01

    In recent years, fuzzy-logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental problems. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. A water quality index calculated with fuzzy reasoning has been developed. The relative importance of water quality indicators involved in the fuzzy inference process has been dealt with a multi-attribute decision-aiding method. The potential application of the fuzzy index has been tested with a case study. A data set collected from the Ebro River (Spain) by two different environmental protection agencies has been used. The current findings, managed within a geographic information system, clearly agree with official reports and expert opinions about the pollution problems in the studied area. Therefore, this methodology emerges as a suitable and alternative tool to be used in developing effective water management plans.

  14. A fuzzy logic system for seizure onset detection in intracranial EEG.

    PubMed

    Rabbi, Ahmed Fazle; Fazel-Rezai, Reza

    2012-01-01

    We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.

  15. Controlling of grid connected photovoltaic lighting system with fuzzy logic

    SciTech Connect

    Saglam, Safak; Ekren, Nazmi; Erdal, Hasan

    2010-02-15

    In this study, DC electrical energy produced by photovoltaic panels is converted to AC electrical energy and an indoor area is illuminated using this energy. System is controlled by fuzzy logic algorithm controller designed with 16 rules. Energy is supplied from accumulator which is charged by photovoltaic panels if its energy would be sufficient otherwise it is supplied from grid. During the 1-week usage period at the semester time, 1.968 kWh energy is used from grid but designed system used 0.542 kWh energy from photovoltaic panels at the experiments. Energy saving is determined by calculations and measurements for one education year period (9 months) 70.848 kWh. (author)

  16. Switching fuzzy controller design based on switching Lyapunov function for a class of nonlinear systems.

    PubMed

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

    2006-02-01

    This paper presents a switching fuzzy controller design for a class of nonlinear systems. A switching fuzzy model is employed to represent the dynamics of a nonlinear system. In our previous papers, we proposed the switching fuzzy model and a switching Lyapunov function and derived stability conditions for open-loop systems. In this paper, we design a switching fuzzy controller. We firstly show that switching fuzzy controller design conditions based on the switching Lyapunov function are given in terms of bilinear matrix inequalities, which is difficult to design the controller numerically. Then, we propose a new controller design approach utilizing an augmented system. By introducing the augmented system which consists of the switching fuzzy model and a stable linear system, the controller design conditions based on the switching Lyapunov function are given in terms of linear matrix inequalities (LMIs). Therefore, we can effectively design the switching fuzzy controller via LMI-based approach. A design example illustrates the utility of this approach. Moreover, we show that the approach proposed in this paper is available in the research area of piecewise linear control.

  17. 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.

  18. Fuzzy adaptive synchronization of uncertain chaotic systems via delayed feedback control

    NASA Astrophysics Data System (ADS)

    Zhang, Lingling; Huang, Lihong; Zhang, Zhizhou; Wang, Zengyun

    2008-09-01

    Based on the T-S fuzzy model and the delayed feedback control (DFC) scheme, this Letter presents a robust synchronization strategy for a class of chaotic system with unknown parameters and disturbances. Being the response system, the designed robust observer can adaptively track the drive system globally. The T-S fuzzy model of the 4D chaotic system (Lorenz-Stenflo) is developed as an example for illustration. Numerical simulations are shown to verify the results.

  19. Rocket engine system reliability analyses using probabilistic and fuzzy logic techniques

    NASA Technical Reports Server (NTRS)

    Hardy, Terry L.; Rapp, Douglas C.

    1994-01-01

    The reliability of rocket engine systems was analyzed by using probabilistic and fuzzy logic techniques. Fault trees were developed for integrated modular engine (IME) and discrete engine systems, and then were used with the two techniques to quantify reliability. The IRRAS (Integrated Reliability and Risk Analysis System) computer code, developed for the U.S. Nuclear Regulatory Commission, was used for the probabilistic analyses, and FUZZYFTA (Fuzzy Fault Tree Analysis), a code developed at NASA Lewis Research Center, was used for the fuzzy logic analyses. Although both techniques provided estimates of the reliability of the IME and discrete systems, probabilistic techniques emphasized uncertainty resulting from randomness in the system whereas fuzzy logic techniques emphasized uncertainty resulting from vagueness in the system. Because uncertainty can have both random and vague components, both techniques were found to be useful tools in the analysis of rocket engine system reliability.

  20. Robust Takagi-Sugeno fuzzy control for fractional order hydro-turbine governing system.

    PubMed

    Wang, Bin; Xue, Jianyi; Wu, Fengjiao; Zhu, Delan

    2016-11-01

    A robust fuzzy control method for fractional order hydro-turbine governing system (FOHGS) in the presence of random disturbances is investigated in this paper. Firstly, the mathematical model of FOHGS is introduced, and based on Takagi-Sugeno (T-S) fuzzy rules, the generalized T-S fuzzy model of FOHGS is presented. Secondly, based on fractional order Lyapunov stability theory, a novel T-S fuzzy control method is designed for the stability control of FOHGS. Thirdly, the relatively loose sufficient stability condition is acquired, which could be transformed into a group of linear matrix inequalities (LMIs) via Schur complement as well as the strict mathematical derivation is given. Furthermore, the control method could resist random disturbances, which shows the good robustness. Simulation results indicate the designed fractional order T-S fuzzy control scheme works well compared with the existing method.

  1. Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

    NASA Astrophysics Data System (ADS)

    Mehmanpazir, Farhad; Asadi, Shahrokh

    2016-07-01

    The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a "data mining-based evolutionary fuzzy expert system" (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for

  2. Internal insulation system development

    NASA Technical Reports Server (NTRS)

    Gille, J. P.

    1973-01-01

    The development of an internal insulation system for cryogenic liquids is described. The insulation system is based on a gas layer concept in which capillary or surface tension effects are used to maintain a stable gas layer within a cellular core structure between the tank wall and the contained cryogen. In this work, a 1.8 meter diameter tank was insulated and tested with liquid hydrogen. Ability to withstand cycling of the aluminum tank wall to 450 K was a design and test condition.

  3. Using fuzzy logic to integrate neural networks and knowledge-based systems

    NASA Technical Reports Server (NTRS)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  4. 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.

  5. Assessing flood vulnerability using a rule-based fuzzy system.

    PubMed

    Yazdi, J; Neyshabouri, S A A S

    2012-01-01

    Population growth and urbanization in the last decades have increased the vulnerability of properties and societies in flood-prone areas. Vulnerability analysis is one of the main factors used to determine the necessary measures of flood risk reduction in floodplains. At present, the vulnerability of natural disasters is analyzed by defining the various physical and social indices. This study presents a model based on a fuzzy rule-based system to address various ambiguities and uncertainties from natural variability, and human knowledge and preferences in vulnerability analysis. The proposed method is applied for a small watershed as a case study and the obtained results are compared with one of the index approaches. Both approaches present the same ranking for the sub-basin's vulnerability in the watershed. Finally, using the scores of vulnerability in different sub-basins, a vulnerability map of the watershed is presented.

  6. Human activity recognition based on Evolving Fuzzy Systems.

    PubMed

    Iglesias, Jose Antonio; Angelov, Plamen; Ledezma, Agapito; Sanchis, Araceli

    2010-10-01

    Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.

  7. Diagnosis of the industrial systems by fuzzy classification.

    PubMed

    Toscano, R; Lyonnet, P

    2003-04-01

    The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parametrization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parametrization which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a dc motor showing the effectiveness of this method.

  8. 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.

  9. Neural Network and Fuzzy Logic Technology for Naval Flight Control Systems

    DTIC Science & Technology

    1991-08-06

    it is still uncertain what neural network and fuzzy logic functions are both technologically feasible and suitable for flight control system...this program is focused on the development of a neural network FCS design tool, a neural network flight control law emulator, a fuzzy logic automatic...carrier landing system and a neural network flight control configuration management system. For each project, some initial results are given. Also

  10. Study on adaptive PID algorithm of hydraulic turbine governing system based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Tang, Liangbao; Bao, Jumin

    2006-11-01

    The conventional hydraulic turbine governing system can't automatically modulate PID parameters according to the dynamic process of the system, the generator speed is unstable and the mains frequency fluctuation results in. To solve the above problem, the fuzzy neural network (FNN) and the adaptive control are combined to design an adaptive PID algorithm based on the fuzzy neural network which can effectively control the hydraulic turbine governing system. Finally, the improved mathematic model is simulated. The simulation results are compared with the conventional hydraulic turbine's. Thus the validity and superiority of the fuzzy neural network PID algorithm have been proved. The simulation results show that the algorithm not only retains the functions of fuzzy control, but also provides the ability to approach to the non-linear system. Also the dynamic process of the system can be reflected more precisely and the on-line adaptive control is implemented. The algorithm is superior to other methods in response and control effect.

  11. Adaptive Fuzzy Control for Uncertain Fractional-Order Financial Chaotic Systems Subjected to Input Saturation

    PubMed Central

    Wang, Chenhui

    2016-01-01

    In this paper, control of uncertain fractional-order financial chaotic system with input saturation and external disturbance is investigated. The unknown part of the input saturation as well as the system’s unknown nonlinear function is approximated by a fuzzy logic system. To handle the fuzzy approximation error and the estimation error of the unknown upper bound of the external disturbance, fractional-order adaptation laws are constructed. Based on fractional Lyapunov stability theorem, an adaptive fuzzy controller is designed, and the asymptotical stability can be guaranteed. Finally, simulation studies are given to indicate the effectiveness of the proposed method. PMID:27783648

  12. 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.

  13. Takagi-Sugeno fuzzy modeling and chaos control of partial differential systems

    NASA Astrophysics Data System (ADS)

    Vasegh, Nastaran; Khellat, Farhad

    2013-12-01

    In this paper a unified approach is presented for controlling chaos in nonlinear partial differential systems by a fuzzy control design. First almost all known chaotic partial differential equation systems are represented by Takagi-Sugeno fuzzy model. For investigating design procedure, Kuramoto-Sivashinsky (K-S) equation is selected. Then, all linear subsystems of K-S equation are transformed to ordinary differential equation (ODE) systems by truncated Fourier series of sine-cosine functions. By solving Riccati equation for each ODE systems, parallel stabilizing feedback controllers are determined. Finally, a distributed fuzzy feedback for K-S equation is designed. Numerical simulations are given to show that the distributed fuzzy controller is very easy to design, efficient, and capable to extend.

  14. Takagi-Sugeno fuzzy modeling and chaos control of partial differential systems.

    PubMed

    Vasegh, Nastaran; Khellat, Farhad

    2013-12-01

    In this paper a unified approach is presented for controlling chaos in nonlinear partial differential systems by a fuzzy control design. First almost all known chaotic partial differential equation systems are represented by Takagi-Sugeno fuzzy model. For investigating design procedure, Kuramoto-Sivashinsky (K-S) equation is selected. Then, all linear subsystems of K-S equation are transformed to ordinary differential equation (ODE) systems by truncated Fourier series of sine-cosine functions. By solving Riccati equation for each ODE systems, parallel stabilizing feedback controllers are determined. Finally, a distributed fuzzy feedback for K-S equation is designed. Numerical simulations are given to show that the distributed fuzzy controller is very easy to design, efficient, and capable to extend.

  15. Adaptive fuzzy fault-tolerant output feedback control of uncertain nonlinear systems with actuator faults

    NASA Astrophysics Data System (ADS)

    Huo, Baoyu; Tong, Shaocheng; Li, Yongming

    2013-12-01

    This article develops an adaptive fuzzy control method for accommodating actuator faults in a class of unknown nonlinear systems with unmeasured states. The considered faults are modelled as both loss of effectiveness and lock-in-place (stuck at unknown place). With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is developed for estimating the unmeasured states. Combining the backstepping technique with the nonlinear tolerant-fault control theory, a novel adaptive fuzzy faults-tolerant control approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the tracking error between the system output and the reference signal converges to a small neighbourhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach.

  16. A Fuzzy-Neuro Scheme for Fault Diagnosis and Life Consumption of Rotordynamic Systems

    DTIC Science & Technology

    1996-04-01

    O6B DTIC Information For The Defense CommunrtY 000MWPP A FUZZY-NEURO SCHEME FOR FAULT DIAGNOSIS AND LIFE CONSUMPTION OF ROTORDYNAMIC SYSTEMS Michael J... rotordynamic , finite-etement modeling. A rotor demonstration rig is used as a proof of concept tool. The approach integrates rotor shaft vibration...measurements with detailed, rotordynamic , finite-element models through a fuzzy-neuro scheme which is specifically developed to respond to the rotor system

  17. Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

    NASA Astrophysics Data System (ADS)

    Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

    2017-03-01

    Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

  18. Finite-dimensional constrained fuzzy control for a class of nonlinear distributed process systems.

    PubMed

    Wu, Huai-Ning; Li, Han-Xiong

    2007-10-01

    This correspondence studies the problem of finite-dimensional constrained fuzzy control for a class of systems described by nonlinear parabolic partial differential equations (PDEs). Initially, Galerkin's method is applied to the PDE system to derive a nonlinear ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, a systematic modeling procedure is given to construct exactly a Takagi-Sugeno (T-S) fuzzy model for the finite-dimensional ODE system under state constraints. Then, based on the T-S fuzzy model, a sufficient condition for the existence of a stabilizing fuzzy controller is derived, which guarantees that the state constraints are satisfied and provides an upper bound on the quadratic performance function for the finite-dimensional slow system. The resulting fuzzy controllers can also guarantee the exponential stability of the closed-loop PDE system. Moreover, a local optimization algorithm based on the linear matrix inequalities is proposed to compute the feedback gain matrices of a suboptimal fuzzy controller in the sense of minimizing the quadratic performance bound. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.

  19. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    SciTech Connect

    Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  20. Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm.

    PubMed

    Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei

    2014-04-01

    In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

  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. 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.

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

    PubMed

    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.

  4. Adaptive fuzzy decentralised control for stochastic nonlinear large-scale systems in pure-feedback form

    NASA Astrophysics Data System (ADS)

    Tong, Shaocheng; Xu, Yinyin; Li, Yongming

    2015-06-01

    This paper is concerned with the problem of adaptive fuzzy decentralised output-feedback control for a class of uncertain stochastic nonlinear pure-feedback large-scale systems with completely unknown functions, the mismatched interconnections and without requiring the states being available for controller design. With the help of fuzzy logic systems approximating the unknown nonlinear functions, a fuzzy state observer is designed estimating the unmeasured states. Therefore, the nonlinear filtered signals are incorporated into the backstepping recursive design, and an adaptive fuzzy decentralised output-feedback control scheme is developed. It is proved that the filter system converges to a small neighbourhood of the origin based on appropriate choice of the design parameters. Simulation studies are included illustrating the effectiveness of the proposed approach.

  5. 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.

  6. The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller

    NASA Astrophysics Data System (ADS)

    Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin

    The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.

  7. Fuzzy neural network technique for system state forecasting.

    PubMed

    Li, Dezhi; Wang, Wilson; Ismail, Fathy

    2013-10-01

    In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

  8. An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems.

    PubMed

    Uzoka, Faith-Michael Emeka; Obot, Okure; Barker, Ken; Osuji, J

    2011-07-01

    The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP.

  9. International Space Station Power Systems

    NASA Technical Reports Server (NTRS)

    Propp, Timothy William

    2001-01-01

    This viewgraph presentation gives a general overview of the International Space Station Power Systems. The topics include: 1) The Basics of Power; 2) Space Power Systems Design Constraints; 3) Solar Photovoltaic Power Systems; 4) Energy Storage for Space Power Systems; 5) Challenges of Operating Power Systems in Earth Orbit; 6) and International Space Station Electrical Power System.

  10. Fuzzy Rule Suram for Control System of a Solar Energy Wood Drying Chamber

    NASA Astrophysics Data System (ADS)

    Situmorang, Zakarias; Wardoyo, Retantyo; Hartati, Sri; Eko Istiyanto, Jazi

    2009-08-01

    The paper reports used the fuzzy rule Suram for control system of a wood drying chamber with solar as source of energy. Rule suram based of fuzzy logic with variables of weather is temperature ambient and conditions of air is humidity ambient, it implemented for wood drying process. The membership function of variable of state represented in error value and change error with typical of triangle and trapezium map. Result of Analysis to reach 8 fuzzy rule to control the output system can be constructed in a number of way of weather and conditions of air. It used to minimum of the consumption of electric energy by heater. The rule suram used to stability and equilibrium of schedule of drying in chamber by control of temperature and humidity. The result of implemented of fuzzy rule suram with the modification of membership function in range [0.5, 1] represented approximate to he conditions riel.

  11. Neural-network-based fuzzy logic control system with applications on compliant robot control

    NASA Astrophysics Data System (ADS)

    Hor, MawKae; Lu, Hui L.

    1994-10-01

    In view of the success of neural network applications in inverted pendulum control, speech recognition, and other problem solving, we believe that one could inject the noise removing concepts and learning spirits into the algorithm in constructing the neural networks and apply it to the various tasks such as compliant coordinated motion using multiple robots. Based on the fuzzy logic, a fuzzy logical control system is a logical system which is much closer to human thinking than any other logical systems. During recent years, fuzzy logic control has emerged as a fruitful area in applications, especially the applications lacking quantitative data regarding the input-output relations. Whereas, the connectionist model injects the learning ability to the fuzzy logic system. This model, proposed by Lin and Lee, is a connected neural network that embedded the fuzzy rules in the architecture. Since this model is general enough and we expect the embedded fuzzy concepts can solve the problems caused by the defective training data, it is chosen as our base structure. Appropriate modifications have been made to this model to reflect the real situations encountered in the robot applications. Our goal is to control two different types of robots for coordinated motion using sensory feedback information.

  12. The cognitive bases for the design of a new class of fuzzy logic controllers: The clearness transformation fuzzy logic controller

    NASA Technical Reports Server (NTRS)

    Sultan, Labib; Janabi, Talib

    1992-01-01

    This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.

  13. An expert system for fault section diagnosis of power systems using fuzzy relations

    SciTech Connect

    Cho, H.J.; Park, J.K.

    1997-02-01

    This paper proposes an expert system using fuzzy relations to deal with uncertainties imposed on fault section diagnosis of power systems. The authors build sagittal diagrams which represent the fuzzy relations for power systems, and diagnose fault sections using the sagittal diagrams. Next, they examine the malfunction or wrong alarm of relays and circuit breakers based on the alarm information and the estimated fault section. The proposed system provides the fault section candidates in terms of the degree of membership and the malfunction or wrong alarm. An operator monitors these candidates and is able to diagnose the fault section, coping with uncertainties. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose faults that have uncertainty.

  14. Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic

    DOEpatents

    Feddema, John T.; Driessen, Brian J.; Kwok, Kwan S.

    2002-01-01

    A decentralized fuzzy logic control system for one vehicle or for multiple robotic vehicles provides a way to control each vehicle to converge on a goal without collisions between vehicles or collisions with other obstacles, in the presence of noisy input measurements and a limited amount of compute-power and memory on board each robotic vehicle. The fuzzy controller demonstrates improved robustness to noise relative to an exact controller.

  15. Depth Control of Sevofluorane Anesthesia with Microcontroller Based Fuzzy Logic System

    DTIC Science & Technology

    2007-11-02

    sevoflurane in humans”, Anesthesiology, 66:301-303, 1987 [10].YARDIMCI, A., ONURAL A.,”Fuzzy Logic Control of Child Blood Pressure During Anaesthesia...microcontroller-based fuzzy logic control system according to the blood pressure and heart rate taken from the patient. The potential benefits of the... blood pressure and hearth rate. The main reason for automating the control of depth anesthesia is to release the anesthesiologist so that he or

  16. 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.

  17. Application of fuzzy system theory in addressing the presence of uncertainties

    SciTech Connect

    Yusmye, A. Y. N.; Goh, B. Y.; Adnan, N. F.; Ariffin, A. K.

    2015-02-03

    In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.

  18. A Fuzzy Reasoning Design for Fault Detection and Diagnosis of a Computer-Controlled System.

    PubMed

    Ting, Y; Lu, W B; Chen, C H; Wang, G K

    2008-03-01

    A Fuzzy Reasoning and Verification Petri Nets (FRVPNs) model is established for an error detection and diagnosis mechanism (EDDM) applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree (FRDT) and a Petri nets (PNs) technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstratrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program.

  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. Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System.

    PubMed

    Wang, Ning; Er, Meng Joo; Sun, Jing-Chao; Liu, Yan-Cheng

    2016-07-01

    In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

  1. Classification of Microarray Data Using Kernel Fuzzy Inference System.

    PubMed

    Kumar, Mukesh; Kumar Rath, Santanu

    2014-01-01

    The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function.

  2. Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.

    PubMed

    Mansoor Baig, Mirza; Gholamhosseini, Hamid; Harrison, Michael J

    2013-07-01

    Anaesthesia monitoring involves critical diagnostic tasks carried out amongst lots of distractions. Computers are capable of handling large amounts of data at high speed and therefore decision support systems and expert systems are now capable of processing many signals simultaneously in real time. We have developed two fuzzy logic based anaesthesia monitoring systems; a real time smart anaesthesia alarm system (RT-SAAM) and fuzzy logic monitoring system-2 (FLMS-2), an updated version of FLMS for the detection of absolute hypovolaemia. This paper presents the design aspects of these two systems which employ fuzzy logic techniques to detect absolute hypovolaemia, and compares their performances in terms of usability and acceptability. The interpretation of these two systems of absolute hypovolaemia was compared with clinicians' assessments using Kappa analysis, RT-SAAM K=0.62, FLMS-2 K=0.75; an improvement in performance by FLMS-2.

  3. 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%.

  4. A new method for generating an invariant iris private key based on the fuzzy vault system.

    PubMed

    Lee, Youn Joo; Park, Kang Ryoung; Lee, Sung Joo; Bae, Kwanghyuk; Kim, Jaihie

    2008-10-01

    Cryptographic systems have been widely used in many information security applications. One main challenge that these systems have faced has been how to protect private keys from attackers. Recently, biometric cryptosystems have been introduced as a reliable way of concealing private keys by using biometric data. A fuzzy vault refers to a biometric cryptosystem that can be used to effectively protect private keys and to release them only when legitimate users enter their biometric data. In biometric systems, a critical problem is storing biometric templates in a database. However, fuzzy vault systems do not need to directly store these templates since they are combined with private keys by using cryptography. Previous fuzzy vault systems were designed by using fingerprint, face, and so on. However, there has been no attempt to implement a fuzzy vault system that used an iris. In biometric applications, it is widely known that an iris can discriminate between persons better than other biometric modalities. In this paper, we propose a reliable fuzzy vault system based on local iris features. We extracted multiple iris features from multiple local regions in a given iris image, and the exact values of the unordered set were then produced using the clustering method. To align the iris templates with the new input iris data, a shift-matching technique was applied. Experimental results showed that 128-bit private keys were securely and robustly generated by using any given iris data without requiring prealignment.

  5. Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish.

    PubMed

    Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C

    2013-03-01

    This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies.

  6. 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.

  7. 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.

  8. Fuzzy coordinator compensation for balancing control of cart-seesaw system

    NASA Astrophysics Data System (ADS)

    Lin, J.; Guo, S.-Y.; Chang, Julian

    2011-12-01

    In contrast with fully controllable systems, a super articulated mechanical system (SAMS) is a controlled underactuated mechanical system in which the dimensions of the configuration space exceed the dimensions of the control input space. The control of the cart-seesaw system is especially difficult since it is an underactuated mechanism (three degrees of freedom and only two inputs). This research develops a balancing approach for a novel SAMS model, called the cart-seesaw system, using fuzzy logic and fuzzy coordinator compensation to drive the sliding carts and keep the seesaw angle close to zero in the equilibrium state. Experimental results indicate that utilizing the proposed control methodology significantly enhances the performance. Moreover, the presentation of the fuzzy balancing controller is not considerably affected by changes in the environmental parameters, which demonstrates the effectiveness of the fuzzy controller in minimizing the seesaw tilt angle in the time domain, although the system is caused by unpredicted loading variation. Moreover, the experimental results indicate the usefulness and robustness of the proposed fuzzy control methodology. Furthermore, the proposed software/hardware platform can be beneficial for standardizing laboratory equipment and developing amusement apparatus.

  9. 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.

  10. Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults

    NASA Astrophysics Data System (ADS)

    Hou, Yingxue; Tong, Shaocheng; Li, Yongming

    2016-11-01

    This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.

  11. Fuzzy learning vector quantization neural network and its application for artificial odor recognition system

    NASA Astrophysics Data System (ADS)

    Kusumoputro, Benyamin; Budiarto, Hary; Jatmiko, Wisnu

    2000-03-01

    In this paper, a kind of fuzzy algorithm for learning vector quantization is developed and used as pattern classifiers with a supervised learning paradigm in artificial odor discrimination system. In this type of FLVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistical of the measurement error directly. During learning,the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated in two ways. Firstly, by shifting the central position of the fuzzy reference vector toward or away from the input vector, and secondly, by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of FLVQ is different in nature with FALVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ provided high recognition probability in determining various learn-category of odors, however, the FNLVQ neural system has the ability to recognize the unlearn-category of odor that could not recognized by FALVQ neural system.

  12. Nonlinear dynamics in coupled fuzzy control systems I. Coherence and chaos-frustration in triangle configuration

    NASA Astrophysics Data System (ADS)

    Takatsuka, Kazuo

    Nonlinear dynamics and chaos are studied in a system for which a complete set of equations of motion such as equations of Newton, Navier-Stokes and Van der Pol, is not available. As a very general system as such, we consider coupled classical spins (pendulums), each of which is under control by a fuzzy system that is designed to align the spin to an unstable fixed point. The fuzzy system provides a deterministic procedure to control an object without use of a differential equation. The positions and velocities of the spins are monitored periodically and each fuzzy control gives a momentum to its associated spin in the reverse directions. If the monitoring is made with an interval short enough, the spin-spin interactions are overwhelmed by the fuzzy control and the system converges to a state as designed. However, a long-interval monitoring induces dynamics of “too-late response”, and thereby results in chaos. A great variety of dynamics are generated under very delicate balance between the fuzzy control and the spin-spin interaction, in which two independent mechanisms of creating negative and positive “Liapunov exponents” interact with each other.

  13. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    NASA Astrophysics Data System (ADS)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  14. Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO.

    PubMed

    Pan, Indranil; Das, Saptarshi

    2016-05-01

    This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

  15. Performance analysis of complex repairable industrial systems using PSO and fuzzy confidence interval based methodology.

    PubMed

    Garg, Harish

    2013-03-01

    The main objective of the present paper is to propose a methodology for analyzing the behavior of the complex repairable industrial systems. In real-life situations, it is difficult to find the most optimal design policies for MTBF (mean time between failures), MTTR (mean time to repair) and related costs by utilizing available resources and uncertain data. For this, the availability-cost optimization model has been constructed for determining the optimal design parameters for improving the system design efficiency. The uncertainties in the data related to each component of the system are estimated with the help of fuzzy and statistical methodology in the form of the triangular fuzzy numbers. Using these data, the various reliability parameters, which affects the system performance, are obtained in the form of the fuzzy membership function by the proposed confidence interval based fuzzy Lambda-Tau (CIBFLT) methodology. The computed results by CIBFLT are compared with the existing fuzzy Lambda-Tau methodology. Sensitivity analysis on the system MTBF has also been addressed. The methodology has been illustrated through a case study of washing unit, the main part of the paper industry.

  16. Comparing International Curriculum Systems: The International Instructional Systems Study

    ERIC Educational Resources Information Center

    Creese, Brian; Gonzalez, Alvaro; Isaacs, Tina

    2016-01-01

    This paper sets out the main findings of the International Instructional Systems Study (IISS), conducted by the UCL Institute of Education and funded by the Center on International Education Benchmarking (CIEB). The study examined the instructional systems and intended curricula of six "high performing" countries and two US states. The…

  17. An integrated fuzzy inference based monitoring, diagnostic, and prognostic system

    NASA Astrophysics Data System (ADS)

    Garvey, Dustin

    To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable "grab bag" of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1

  18. Observer-Based Output-Feedback Asynchronous Control for Switched Fuzzy Systems.

    PubMed

    Wang, Tiechao; Tong, Shaocheng

    2016-05-10

    This paper investigates an output-feedback control design problem for a class of switched continuous-time Takagi-Sugeno (T-S) fuzzy systems. The considered fuzzy systems consist of several switching modes and each switching mode is described by T-S fuzzy models. In addition, there exists the asynchronous switching between the system switching modes and the controller switching modes. By using parallel distributed compensation design method, the output-feedback control schemes are developed based on state observers for the measurable and immeasurable premise variables cases. The sufficient conditions of ensuring the switched control system stabilization are proposed based on the theory of Lyapunov stability and average-dwell time methods. The controller and observer gains are obtained via two-step method. An illustrated numerical example is provided to show the effectiveness of the proposed control approaches.

  19. Adaptive fuzzy switched control design for uncertain nonholonomic systems with input nonsmooth constraint

    NASA Astrophysics Data System (ADS)

    Li, Yongming; Tong, Shaocheng

    2016-10-01

    In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated with x0(t0) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced to n and the 'explosion of complexity' problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach.

  20. Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory

    NASA Astrophysics Data System (ADS)

    Rigatos, Gerasimos G.

    2013-10-01

    An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

  1. H∞ consensus and synchronization of nonlinear systems based on a novel fuzzy model.

    PubMed

    Zhao, Yan; Li, Bing; Qin, Jiahu; Gao, Huijun; Karimi, Hamid Reza

    2013-12-01

    This paper investigates the H∞ consensus control problem of nonlinear multiagent systems under an arbitrary topological structure. A novel Takagi-Sukeno (T-S) fuzzy modeling method is proposed to describe the problem of nonlinear follower agents approaching a time-varying leader, i.e., the error dynamics between the follower agents and the leader, whose dynamics is evolving according to an isolated unforced nonlinear agent model, is described as a set of T-S fuzzy models. Based on the model, a leader-following consensus algorithm is designed so that, under an arbitrary network topology, all the follower agents reach consensus with the leader subject to external disturbances, preserving a guaranteed H(∞) performance level. In addition, we obtain a sufficient condition for choosing the pinned nodes to make the entire multiagent network reach consensus. Moreover, the fuzzy modeling method is extended to solve the synchronization problem of nonlinear systems, and a fuzzy H(∞) controller is designed so that two nonlinear systems reach synchronization with a prescribed H(∞) performance level. The controller design procedure is greatly simplified by utilization of the proposed fuzzy modeling method. Finally, numerical simulations on chaotic systems and arbitrary nonlinear functions are provided to illustrate the effectiveness of the obtained theoretical results.

  2. Automatic Road Gap Detection Using Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

    2011-09-01

    Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

  3. 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.

  4. A fuzzy approach for modelling radionuclide in lake system.

    PubMed

    Desai, H K; Christian, R A; Banerjee, J; Patra, A K

    2013-10-01

    Radioactive liquid waste is generated during operation and maintenance of Pressurised Heavy Water Reactors (PHWRs). Generally low level liquid waste is diluted and then discharged into the near by water-body through blowdown water discharge line as per the standard waste management practice. The effluents from nuclear installations are treated adequately and then released in a controlled manner under strict compliance of discharge criteria. An attempt was made to predict the concentration of (3)H released from Kakrapar Atomic Power Station at Ratania Regulator, about 2.5 km away from the discharge point, where human exposure is expected. Scarcity of data and complex geometry of the lake prompted the use of Heuristic approach. Under this condition, Fuzzy rule based approach was adopted to develop a model, which could predict (3)H concentration at Ratania Regulator. Three hundred data were generated for developing the fuzzy rules, in which input parameters were water flow from lake and (3)H concentration at discharge point. The Output was (3)H concentration at Ratania Regulator. These data points were generated by multiple regression analysis of the original data. Again by using same methodology hundred data were generated for the validation of the model, which were compared against the predicted output generated by using Fuzzy Rule based approach. Root Mean Square Error of the model came out to be 1.95, which showed good agreement by Fuzzy model of natural ecosystem.

  5. Computer control system based on fuzzy control for boilers

    NASA Astrophysics Data System (ADS)

    Zheng, Dezhong; Shang, Liping; Shi, Jinghao

    2000-10-01

    According tp the features of the combustion process of boiler the optimization of combustion is implemented by using fuzzy control principle. The paper states a control strategy implementing different control regulation in different phases (coarse, fine and precision tuning) for enhancing the thermal efficiency of combustion of boiler. The practice shows that the thermal efficiency increased 2.8%.

  6. Applying Dynamic Fuzzy Petri Net to Web Learning System

    ERIC Educational Resources Information Center

    Chen, Juei-Nan; Huang, Yueh-Min; Chu, William

    2005-01-01

    This investigation presents a DFPN (Dynamic Fuzzy Petri Net) model to increase the flexibility of the tutoring agent's behaviour and thus provide a learning content structure for a lecture course. The tutoring agent is a software assistant for a single user, who may be an expert in an e-Learning course. Based on each learner's behaviour, the…

  7. Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

    NASA Astrophysics Data System (ADS)

    Prakash, S.; Sinha, S. K.

    2015-09-01

    In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

  8. 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.

  9. 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.

  10. A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

    PubMed

    Butt, Muhammad Arif; Akram, Muhammad

    2016-01-01

    We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.

  11. Distributed Proportional-spatial Derivative control of nonlinear parabolic systems via fuzzy PDE modeling approach.

    PubMed

    Wang, Jun-Wei; Wu, Huai-Ning; Li, Han-Xiong

    2012-06-01

    In this paper, a distributed fuzzy control design based on Proportional-spatial Derivative (P-sD) is proposed for the exponential stabilization of a class of nonlinear spatially distributed systems described by parabolic partial differential equations (PDEs). Initially, a Takagi-Sugeno (T-S) fuzzy parabolic PDE model is proposed to accurately represent the nonlinear parabolic PDE system. Then, based on the T-S fuzzy PDE model, a novel distributed fuzzy P-sD state feedback controller is developed by combining the PDE theory and the Lyapunov technique, such that the closed-loop PDE system is exponentially stable with a given decay rate. The sufficient condition on the existence of an exponentially stabilizing fuzzy controller is given in terms of a set of spatial differential linear matrix inequalities (SDLMIs). A recursive algorithm based on the finite-difference approximation and the linear matrix inequality (LMI) techniques is also provided to solve these SDLMIs. Finally, the developed design methodology is successfully applied to the feedback control of the Fitz-Hugh-Nagumo equation.

  12. A fuzzy intelligent system for land consolidation - a case study in Shunde, China

    NASA Astrophysics Data System (ADS)

    Wang, J.; Ge, A.; Hu, Y.; Li, C.; Wang, L.

    2015-08-01

    Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision-making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. A group of indexes need to be selected for land consolidation. In this article, a fuzzy measure was adopted to accomplish the selection of specific features. A fuzzy integral based on a fuzzy measure is a type of fusion tool. The optimal solution with the fewest non-zero elements was obtained for the fuzzy measure by solving a fuzzy integral. This algorithm provides a quick and optimal way to identify the land-index system when preparing to conduct land consolidation. This new research was applied to Shunde's "Three Old" consolidation project which provides the data. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove to be consistent, and the new model is more automatic and intelligent. The results of this estimation system are significant for informing decision-making in land consolidation.

  13. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2010-09-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  14. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    NASA Astrophysics Data System (ADS)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2011-01-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  15. Developing a new transformer fault diagnosis system through evolutionary fuzzy logic

    SciTech Connect

    Huang, Y.C.; Huang, C.L.; Yang, H.T.

    1997-04-01

    To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

  16. Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

    NASA Technical Reports Server (NTRS)

    Murphy, Michael G.

    1991-01-01

    Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system.

  17. Modeling fuzzy state space of reheater system for simulation and analysis

    NASA Astrophysics Data System (ADS)

    Munirah, W. M. Wan; Ahmad, T.; Ashaari, A.; Abdullah, M. Adib

    2014-07-01

    Reheater is one of the important heat exchange components in a high capacity power plant of a boiler system. The aim of this study is to improve heat transfer of a reheater system. The method is to maximize steam production and at the same time, keeping variables within constraints. Fuzzy arithmetic is a powerful tool used to solve engineering problems with uncertain parameters. Therefore, in order to determine heat transfer efficiency, the state space of reheater is simulated using fuzzy arithmetic by taking into account the uncertainties in the reheater system. The uncertain model parameters and the model inputs are represented by fuzzy numbers with their shape derived from quasi-Gaussian function. Finally, this paper discusses how the mathematical model can be manipulated in order to produce maximum heat transfer with least loss of energy. Furthermore, the improvement of the reheater efficiency and the quantification of the heat supplied parameters are presented in this paper.

  18. 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.

  19. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

    PubMed

    Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

    2016-01-01

    Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

  20. Induction machine Direct Torque Control system based on fuzzy adaptive control

    NASA Astrophysics Data System (ADS)

    Li, Shi-ping; Yu, Yan; Jiao, Zhen-gang; Gu, Shu-sheng

    2009-07-01

    Direct Torque Control technology is a high-performance communication control method, it uses the space voltage vector method, and then to the inverter switch state control, to obtain high torque dynamic performance. But none of the switching states is able to generate the exact voltage vector to produce the desired changes in torque and flux in most of the switching instances. This causes a high ripple in torque. To solve this problem, a fuzzy implementation of Direct Torque Control of Induction machine is presented here. Error of stator flux, error of motor electromagnetic torque and position of angle of flux are taken as fuzzy variables. In order to further solve nonlinear problem of variation parameters in direct torque control system, the paper proposes a fuzzy parameter PID adaptive control method which is suitable for the direct torque control of an asynchronous motor. The generation of its fuzzy control is obtained by analyzing and optimizing PID control step response and combining expert's experience. For this reason, it carries out fuzzy work to PID regulator of motor speed to achieve to regulate PID parameters. Therefore the control system gets swifter response velocity, stronger robustness and higher precision of velocity control. The computer simulated results verify the validity of this novel method.

  1. Crop parameters estimation by fuzzy inference system using X-band scatterometer data

    NASA Astrophysics Data System (ADS)

    Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.

    2013-03-01

    Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.

  2. Controlling Discrete Time T-S Fuzzy Chaotic Systems via Adaptive Adjustment

    NASA Astrophysics Data System (ADS)

    Nian, Yibei; Zheng, Yongai

    In order to overcome typical drawbacks of the OGY control, i.e. the long waiting time for control to be applied and the accessible turning system parameter in advance, this paper presents a new chaos control method based on Takagi- Sugeno (T-S) fuzzy model and adaptive adjustment. This method represents a chaotic system by linear models in different state space regions based on T-S fuzzy model and then stabilize the linear models in different state space regions by the adaptive adjustment mechanism. An example for the Henon map is given to demonstrate the effectiveness of the proposed method.

  3. Intelligent control of PV system on the basis of the fuzzy recurrent neuronet*

    NASA Astrophysics Data System (ADS)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    This paper presents the fuzzy recurrent neuronet for PV system’s control. Based on the PV system’s state, the fuzzy recurrent neural net tracks the maximum power point under random perturbations. The validity and advantages of the proposed intelligent control of PV system are demonstrated by numerical simulations. The simulation results show that the proposed intelligent control of PV system achieves real-time control speed and competitive performance, as compared to a classical control scheme on the basis of the perturbation & observation algorithm.

  4. Robust Filtering for Nonlinear Nonhomogeneous Markov Jump Systems by Fuzzy Approximation Approach.

    PubMed

    Yin, Yanyan; Shi, Peng; Liu, Fei; Teo, Kok Lay; Lim, Cheng-Chew

    2015-09-01

    This paper addresses the problem of robust fuzzy L2-L∞ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering error dynamic system is stochastically stable and that it has a prescribed L2-L∞ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.

  5. Modeling the Retrieval Process for an Information Retrieval System Using an Ordinal Fuzzy Linguistic Approach.

    ERIC Educational Resources Information Center

    Herrera-Viedma, E.

    2001-01-01

    Proposes a linguistic model for an Information Retrieval System (IRS) defined using an ordinal fuzzy linguistic approach. The query subsystem accepts Boolean queries with terms weighted by ordinal linguistic values and the evaluation subsystem returns documents arranged in relevance classes labeled with ordinal linguistic values. The system gives…

  6. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    EPA Science Inventory

    The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

  7. Adaptive hierarchical fuzzy controller

    SciTech Connect

    Raju, G.V.S.; Jun Zhou

    1993-07-01

    A methodology for designing adaptive hierarchical fuzzy controllers is presented. In order to evaluate this concept, several suitable performance indices were developed and converted to linguistic fuzzy variables. Based on those variables, a supervisory fuzzy rule set was constructed and used to change the parameters of a hierarchical fuzzy controller to accommodate the variations of system parameters. The proposed algorithm was used in feedwater flow control to a steam generator. Simulation studies are presented that illustrate the effectiveness of the approach

  8. A fuzzy intelligent system for land consolidation - a case study in Shunde, China

    NASA Astrophysics Data System (ADS)

    Wang, J.; Ge, A.; Hu, Y.; Li, C.; Wang, L.

    2015-04-01

    Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. We needed to select a group of indexes useful for land consolidation according to the concrete demand. In this paper, a fuzzy measure, which can describe the importance of a single feature or a group of features, is adopted to accomplish the selection of specific features. A fuzzy integral that is based on a fuzzy measure is a type of fusion tool. We obtained the optimal solution for a fuzzy measure by solving a fuzzy integral. The fuzzy integrals can be transformed to a set of linear equations. We applied the L1-norm regularization method to solve the linear equations, and we found a solution with the fewest nonzero elements for the fuzzy measure; this solution shows the contribution of corresponding features or the combinations of decisions. This algorithm provides a quick and optimal way to identify the land index system when preparing to conduct the research, such as we describe herein, on land consolidation. Shunde's "Three Old" consolidation project provides the data for this work. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove

  9. Fuzzy methods in decision making process - A particular approach in manufacturing systems

    NASA Astrophysics Data System (ADS)

    Coroiu, A. M.

    2015-11-01

    We are living in a competitive environment, so we can see and understand that the most of manufacturing firms do the best in order to accomplish meeting demand, increasing quality, decreasing costs, and delivery rate. In present a stake point of interest is represented by the development of fuzzy technology. A particular approach for this is represented through the development of methodologies to enhance the ability to managed complicated optimization and decision making aspects involving non-probabilistic uncertainty with the reason to understand, development, and practice the fuzzy technologies to be used in fields such as economic, engineering, management, and societal problems. Fuzzy analysis represents a method for solving problems which are related to uncertainty and vagueness; it is used in multiple areas, such as engineering and has applications in decision making problems, planning and production. As a definition for decision making process we can use the next one: result of mental processes based upon cognitive process with a main role in the selection of a course of action among several alternatives. Every process of decision making can be represented as a result of a final choice and the output can be represented as an action or as an opinion of choice. Different types of uncertainty can be discovered in a wide variety of optimization and decision making problems related to planning and operation of power systems and subsystems. The mixture of the uncertainty factor in the construction of different models serves for increasing their adequacy and, as a result, the reliability and factual efficiency of decisions based on their analysis. Another definition of decision making process which came to illustrate and sustain the necessity of using fuzzy method: the decision making is an approach of choosing a strategy among many different projects in order to achieve some purposes and is formulated as three different models: high risk decision, usual risk

  10. Design of Fuzzy Functional Observer-Controller via Higher Order Derivatives of Lyapunov Function for Nonlinear Systems.

    PubMed

    Liu, Chuang; Lam, Hak-Keung; Fernando, Tyrone; Iu, Herbert Ho-Ching

    2016-05-02

    In this paper, we investigate the stability of Takagi-Sugeno fuzzy-model-based (FMB) functional observer-control system. When system states are not measurable for state-feedback control, a fuzzy functional observer is designed to directly estimate the control input instead of the system states. Although the fuzzy functional observer can reduce the order of the observer, it leads to a number of observer gains to be determined. Therefore, a new form of fuzzy functional observer is proposed to facilitate the stability analysis such that the observer gains can be numerically obtained and the stability can be guaranteed simultaneously. The proposed form is also in favor of applying separation principle to separately design the fuzzy controller and the fuzzy functional observer. To design the fuzzy controller with the consideration of system stability, higher order derivatives of Lyapunov function (HODLF) are employed to reduce the conservativeness of stability conditions. The HODLF generalizes the commonly used first-order derivative. By exploiting the properties of membership functions and the dynamics of the FMB control system, convex and relaxed stability conditions can be derived. Simulation examples are provided to show the relaxation of the proposed stability conditions and the feasibility of designed fuzzy functional observer-controller.

  11. Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes

    NASA Astrophysics Data System (ADS)

    Kim, Ki Wan; Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Lee, Eui Chul; Park, Kang Ryoung

    2015-03-01

    The classification of eye openness and closure has been researched in various fields, e.g., driver drowsiness detection, physiological status analysis, and eye fatigue measurement. For a classification with high accuracy, accurate segmentation of the eye region is required. Most previous research used the segmentation method by image binarization on the basis that the eyeball is darker than skin, but the performance of this approach is frequently affected by thick eyelashes or shadows around the eye. Thus, we propose a fuzzy-based method for classifying eye openness and closure. First, the proposed method uses I and K color information from the HSI and CMYK color spaces, respectively, for eye segmentation. Second, the eye region is binarized using the fuzzy logic system based on I and K inputs, which is less affected by eyelashes and shadows around the eye. The combined image of I and K pixels is obtained through the fuzzy logic system. Third, in order to reflect the effect by all the inference values on calculating the output score of the fuzzy system, we use the revised weighted average method, where all the rectangular regions by all the inference values are considered for calculating the output score. Fourth, the classification of eye openness or closure is successfully made by the proposed fuzzy-based method with eye images of low resolution which are captured in the environment of people watching TV at a distance. By using the fuzzy logic system, our method does not require the additional procedure of training irrespective of the chosen database. Experimental results with two databases of eye images show that our method is superior to previous approaches.

  12. Fuzzy neural order robust of the non-linear systems

    NASA Astrophysics Data System (ADS)

    Madour, F.; Benmahammed, K.

    2008-06-01

    This article introduces a controller at structure of a network multi-layer neurons specified by the fuzzy reasoning of Takagi-Sugeno (TS) order one [1], the weights of the network represent the standard deviations of the membership function. This controller is applied to the ordering of a reversed pendulum. Changes in the entries and the exit, as of the environment changes of operation are introduced in order to test the robustness of the designed controller.

  13. Fuzzy neural order robust of the non-linear systems

    SciTech Connect

    Madour, F.; Benmahammed, K.

    2008-06-12

    This article introduces a controller at structure of a network multi-layer neurons specified by the fuzzy reasoning of Takagi-Sugeno (TS) order one, the weights of the network represent the standard deviations of the membership function. This controller is applied to the ordering of a reversed pendulum. Changes in the entries and the exit, as of the environment changes of operation are introduced in order to test the robustness of the designed controller.

  14. Landslide Susceptibility Assessment Through Fuzzy Logic Inference System (flis)

    NASA Astrophysics Data System (ADS)

    Bibi, T.; Gul, Y.; Rahman, A. Abdul; Riaz, M.

    2016-09-01

    Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

  15. A fuzzy rule base system for object-based feature extraction and classification

    NASA Astrophysics Data System (ADS)

    Jin, Xiaoying; Paswaters, Scott

    2007-04-01

    In this paper, we present a fuzzy rule base system for object-based feature extraction and classification on remote sensing imagery. First, the object primitives are generated from the segmentation step. Object primitives are defined as individual regions with a set of attributes computed on the regions. The attributes computed include spectral, texture and shape measurements. Crisp rules are very intuitive to the users. They are usually represented as "GT (greater than)", "LT (less than)" and "IB (In Between)" with numerical values. The features can be manually generated by querying on the attributes using these crisp rules and monitoring the resulting selected object primitives. However, the attributes of different features are usually overlapping. The information is inexact and not suitable for traditional digital on/off decisions. Here a fuzzy rule base system is built to better model the uncertainty inherent in the data and vague human knowledge. Rather than representing attributes in linguistic terms like "Small", "Medium", "Large", we proposed a new method for automatic fuzzification of the traditional crisp concepts "GT", "LT" and "IB". Two sets of membership functions are defined to model those concepts. One is based on the piecewise linear functions, the other is based on S-type membership functions. A novel concept "fuzzy tolerance" is proposed to control the degree of fuzziness of each rule. The experimental results on classification and extracting features such as water, buildings, trees, fields and urban areas have shown that this newly designed fuzzy rule base system is intuitive and allows users to easily generate fuzzy rules.

  16. Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

    SciTech Connect

    Dumidu Wijayasekara; Ondrej Linda; Milos Manic; Craig Rieger

    2014-08-01

    Building Energy Management Systems (BEMSs) are essential components of modern buildings that utilize digital control technologies to minimize energy consumption while maintaining high levels of occupant comfort. However, BEMSs can only achieve these energy savings when properly tuned and controlled. Since indoor environment is dependent on uncertain criteria such as weather, occupancy, and thermal state, performance of BEMS can be sub-optimal at times. Unfortunately, the complexity of BEMS control mechanism, the large amount of data available and inter-relations between the data can make identifying these sub-optimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD) based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm based BEMS. In six different scenarios, the Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more), that the alarm based BEMS. In addition, the Fuzzy-ADLD method identified cases that were missed by the alarm based system, demonstrating potential for increased state-awareness of abnormal building behavior.

  17. On the evaluation of fuzzy quantified queries in a database management system

    NASA Technical Reports Server (NTRS)

    Bosc, Patrick; Pivert, Olivier

    1992-01-01

    Many propositions to extend database management systems have been made in the last decade. Some of them aim at the support of a wider range of queries involving fuzzy predicates. Unfortunately, these queries are somewhat complex and the question of their efficiency is a subject under discussion. In this paper, we focus on a particular subset of queries, namely those using fuzzy quantified predicates. More precisely, we will consider the case where such predicates apply to individual elements as well as to sets of elements. Thanks to some interesting properties of alpha-cuts of fuzzy sets, we are able to show that the evaluation of these queries can be significantly improved with respect to a naive strategy based on exhaustive scans of sets or files.

  18. Design and tuning of standard additive model based fuzzy PID controllers for multivariable process systems.

    PubMed

    Harinath, Eranda; Mann, George K I

    2008-06-01

    This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system.

  19. How to select combination operators for fuzzy expert systems using CRI

    NASA Technical Reports Server (NTRS)

    Turksen, I. B.; Tian, Y.

    1992-01-01

    A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI.

  20. Supervisory control design based on hybrid systems and fuzzy events detection. Application to an oxichlorination reactor.

    PubMed

    Altamiranda, Edmary; Torres, Horacio; Colina, Eliezer; Chacón, Edgar

    2002-10-01

    This paper presents a supervisory control scheme based on hybrid systems theory and fuzzy events detection. The fuzzy event detector is a linguistic model, which synthesizes complex relations between process variables and process events incorporating experts' knowledge about the process operation. This kind of detection allows the anticipation of appropriate control actions, which depend upon the selected membership functions used to characterize the process under scrutiny. The proposed supervisory control scheme was successfully implemented for an oxichlorination reactor in a vinyl monomer plant. This implementation has allowed improvement of reactor stability and reduction of raw material consumption.

  1. 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.

  2. Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

    This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.

  3. Universal fuzzy integral sliding-mode controllers for stochastic nonlinear systems.

    PubMed

    Gao, Qing; Liu, Lu; Feng, Gang; Wang, Yong

    2014-12-01

    In this paper, the universal integral sliding-mode controller problem for the general stochastic nonlinear systems modeled by Itô type stochastic differential equations is investigated. One of the main contributions is that a novel dynamic integral sliding mode control (DISMC) scheme is developed for stochastic nonlinear systems based on their stochastic T-S fuzzy approximation models. The key advantage of the proposed DISMC scheme is that two very restrictive assumptions in most existing ISMC approaches to stochastic fuzzy systems have been removed. Based on the stochastic Lyapunov theory, it is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time, and moreover, the stochastic stability of the sliding motion can be guaranteed in terms of linear matrix inequalities. Another main contribution is that the results of universal fuzzy integral sliding-mode controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy integral sliding-mode controllers, are provided, respectively. Simulation results from an inverted pendulum example are presented to illustrate the advantages and effectiveness of the proposed approaches.

  4. Adaptive fuzzy PID temperature control system based on single-chip computer for the autoclave

    NASA Astrophysics Data System (ADS)

    Zhang, F.; Wang, J.; Fu, S. L.; He, Z. T.; Li, X. P.

    2008-12-01

    The autoclave is one of main preparation equipments of crystal preparation by hydrothermal method. The preparation temperature will seriously influence crystals quality and crystals size at high temperature, how to measure and control precisely the autoclave temperature can be of real significance. The characteristic of hysteresis, nonlinearity and difficulty to acquire the precise mathematical model existing in the temperature control of the autoclave was researched. The general PID controller adopted usually in the autoclave temperature control system is hard to improve temperature control performance. Based on the advantages of fuzzy controller that does not depend on the precise mathematical model and the stabilization of PID controller, single-chip computer integrated fuzzy PID control algorithm is adopted, and the temperature system is designed, the foundational working principle was discussed. The control system includes SCM (AT89C52), temperature sensor, A/D converter circuit and corresponding circuit and interface, can make the autoclave temperature measure and control accurately. The system hardware includes main circuit, thyristor drive circuit, audible and visual alarm circuit, watchdog circuit, clock circuit, keyboard and display circuit so on, which can achieve gathering, analyzing, comparing and controlling the autoclave temperature parameter. The program of control system includes the treatment and collection of temperature data, the dynamic display program, the fuzzy PID control system, the audible and visual alarm program, et al, and the system's main software, which includes initialization, key-press processing, input processing, display, and the fuzzy PID control program was analyzed. The results showed that the fuzzy PID control system makes the adjustment time of temperature decreased and the precision of temperature control improved, the quality and the crystals size of the preparation crystals can achieve the expect experiment results.

  5. Development of rainfall runoff models using Takagi Sugeno fuzzy inference systems

    NASA Astrophysics Data System (ADS)

    Jacquin, Alexandra P.; Shamseldin, Asaad Y.

    2006-09-01

    SummaryThis study explores the application of Takagi-Sugeno fuzzy inference systems to rainfall-runoff modelling. The models developed intend to describe the non-linear relationship between rainfall as input and runoff as output to the real system using a system based approach. Two types of fuzzy models are proposed, where the first type is intended to account for the effect of changes in catchment wetness in the rainfall-runoff transformation and the second type incorporates seasonality as a source of non-linearity in this relationship. The models developed are applied to data from six catchments of diverse climatic characteristics. 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. The results of this study indicate that fuzzy inference systems are a suitable alternative to the traditional methods for modelling the non-linear relationship between rainfall and runoff.

  6. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    EPA Science Inventory

    The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...

  7. Design of incomplete derivative fuzzy PID control system for fast-steering mirror

    NASA Astrophysics Data System (ADS)

    Ai, Zhiwei; Tan, Yi; Wu, Qiongyan; Ren, Ge; Tan, Yufen; Zhu, Nengbing; Zhu, Fuyin

    2016-10-01

    The structure parameters of fast-steering mirror (FSM) might change with time goes by. In order to reduce the impact of this change on the output performance of FSM system, an incomplete derivative fuzzy PID control system is proposed. This control system can effectively improve the time domain quality of FSM system by optimizing the PID control parameters online. First, the dynamic model of FSM is established. Second, the initial parameters of the incomplete derivative PID control system are designed according to the frequency domain quality of the closed-loop system. Then, the rules and related factors of the fuzzy controller are designed on the basis of the initial parameters. Finally, simulation experiments are carried out. The results show that the incomplete derivative PID control system has shorter adjustment time, less overshoot and lower dependence on the parameters of FSM when compared with the fixed parameters PID control system.

  8. A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring

    SciTech Connect

    Na, Man Gyun; Oh, Seungrohk

    2002-11-15

    A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.

  9. Distribution system load estimation and service restoration using a fuzzy set approach

    SciTech Connect

    Hanching Kuo; Yuanyih, Hsu . Dept. of Electrical Engineering)

    1993-10-01

    An approach based on fuzzy set theory is developed to estimate the loads in a distribution system and to devise a proper service restoration plan following a fault. To estimate the loads on branching points without real-time meters, typical hourly load patterns for several types of days are established for commercial, industrial, and residential customers. These load patterns are characterized by some linguistic variables using fuzzy set notations. The load of a branching point is estimated through fuzzy set operations. With the estimated loads at hand, a heuristic search method is proposed in order to reach a restoration plan with minimal number of switching operations in a short time. To demonstrate the effectiveness of the proposed fuzzy approach, load estimation and service restoration on a distribution system within the service area of Taipei West District Office of Taiwan Power Company are examined. It is found that, following a fault event, a proper restoration plan can be reached very efficiently. Therefore, the proposed approach can provide valuable information to distribution system operators in reaching a service restoration plan.

  10. Fuzzy systems modeling of in situ bioremediation of chlorinatedsolve n ts

    SciTech Connect

    Faybishenko, Boris; Hazen, Terry C.

    2001-09-05

    A large-scale vadose zone-groundwater bioremediationdemonstration was conducted at the Savannah River Site (SRS) by injectingseveral types of gases (ambient air, methane, and nitrous oxide andtriethyl phosphate mixtures) through a horizontal well in the groundwaterat a 175 ft depth. Simultaneously, soil gas was extracted through aparallel horizontal well in the vadose zone at a 80 ft depth Monitoringrevealed a wide range of spatial and temporal variations ofconcentrations of VOCs, enzymes, and biomass in groundwater and vadosezone monitoring boreholes over the field site. One of the powerful modernapproaches to analyze uncertain and imprecise data chemical data is basedon the use of methods of fuzzy systems modeling. Using fuzzy modeling weanalyzed the spatio-temporal TCE and PCE concentrations and methanotrophdensities in groundwater to assess the effectiveness of differentcampaigns of air stripping and bioremediation, and to determine the fuzzyrelationship between these compounds. Our analysis revealed some detailsabout the processes involved in remediation, which were not identified inthe previous studies of the SRS demonstration. We also identified somefuture directions for using fuzzy systems modeling, such as theevaluation of the mass balance of the vadose zone - groundwater system,and the development of fuzzy-ruled methods for optimization of managingremediation activities, predictions, and risk assessment.

  11. 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.

  12. A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

    NASA Astrophysics Data System (ADS)

    Propes, Nicholas C.; Vachtsevanos, George

    2003-08-01

    Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

  13. An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

    NASA Technical Reports Server (NTRS)

    Lin, Paul P.; Jules, Kenol

    2002-01-01

    An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

  14. Recurrent fuzzy ranking methods

    NASA Astrophysics Data System (ADS)

    Hajjari, Tayebeh

    2012-11-01

    With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

  15. Using LDR as Sensing Element for an External Fuzzy Controller Applied in Photovoltaic Pumping Systems with Variable-Speed Drives.

    PubMed

    Maranhão, Geraldo Neves De A; Brito, Alaan Ubaiara; Leal, Anderson Marques; Fonseca, Jéssica Kelly Silva; Macêdo, Wilson Negrão

    2015-09-22

    In the present paper, a fuzzy controller applied to a Variable-Speed Drive (VSD) for use in Photovoltaic Pumping Systems (PVPS) is proposed. The fuzzy logic system (FLS) used is embedded in a microcontroller and corresponds to a proportional-derivative controller. A Light-Dependent Resistor (LDR) is used to measure, approximately, the irradiance incident on the PV array. Experimental tests are executed using an Arduino board. The experimental results show that the fuzzy controller is capable of operating the system continuously throughout the day and controlling the direct current (DC) voltage level in the VSD with a good performance.

  16. Using LDR as Sensing Element for an External Fuzzy Controller Applied in Photovoltaic Pumping Systems with Variable-Speed Drives

    PubMed Central

    Maranhão, Geraldo Neves De A.; Brito, Alaan Ubaiara; Leal, Anderson Marques; Fonseca, Jéssica Kelly Silva; Macêdo, Wilson Negrão

    2015-01-01

    In the present paper, a fuzzy controller applied to a Variable-Speed Drive (VSD) for use in Photovoltaic Pumping Systems (PVPS) is proposed. The fuzzy logic system (FLS) used is embedded in a microcontroller and corresponds to a proportional-derivative controller. A Light-Dependent Resistor (LDR) is used to measure, approximately, the irradiance incident on the PV array. Experimental tests are executed using an Arduino board. The experimental results show that the fuzzy controller is capable of operating the system continuously throughout the day and controlling the direct current (DC) voltage level in the VSD with a good performance. PMID:26402688

  17. An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

    NASA Astrophysics Data System (ADS)

    Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

    2017-03-01

    Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.

  18. Computerized international geothermal information systems

    SciTech Connect

    Phillips, S.L.; Lawrence, J.D.; Lepman, S.R.

    1980-03-01

    The computerized international geothermal energy information system is reviewed. The review covers establishment of the Italy - United States linked data centers by the NATO Committee on Challenges of Modern Society, through a bilateral agreement, and up to the present time. The result of the information exchange project is given as the bibliographic and numerical data available from the data centers. Recommendations for the exchange of computerized geothermal information at the international level are discussed.

  19. A heuristic based fuzzy reasoning approach for distribution system service restoration

    SciTech Connect

    Hsu, Y.Y.; Kuo, H.C. . Dept. of Electrical Engineering)

    1994-04-01

    A fuzzy reasoning approach is proposed for the service restoration of a distribution system. After the location of a fault has been identified and the faulted zone has been isolated, it is important for the operators to reach a proper service restoration plan in order to restore the electricity service outside the faulted zone. The operators tend to use their past experience and heuristic rules to devise such a restoration plan because it must satisfy a lot of practical needs and objectives such as minimal number of switching operations, no interrupted customers, no overloaded components,..., etc. Thus, service restoration is a multiple-objective problem with some objectives contradictory to each other. In most cases, compromise must be made in order to reach a plan which meets the operators' practical needs. In addition, the operators' needs and heuristic rules are often expressed in imprecise linguistic terms. In this paper, fuzzy set notation is employed to deal with these imprecise linguistic variables and a set of fuzzy reasoning procedures are developed to implement the operators' heuristic rules. These procedures can be employed to solve the multiple-objective problem of service restoration described in imprecise linguistic variables. To demonstrate the effectiveness of the proposed fuzzy reasoning approach, service restoration on a distribution system within the service area of Taipei West District Office of Taiwan Power Company is examined. It is found that a proper restoration plan can be reached very efficiently by the proposed approach.

  20. Integration of global and local knowledge for fuzzy expert system creation: application to arrhythmic beat classification.

    PubMed

    Tsipouras, Markos G; Exarchos, Themis P; Fotiadis, Dimitrios I

    2007-01-01

    In this work, we propose a method for the automated expert system creation. The method is based on the integration of global knowledge (i.e. knowledge from the field experts) and local knowledge (i.e. knowledge derived from the available data) in a single inference engine. Starting from an initial set of rules (expert's knowledge) and an annotated dataset, data mining is performed to the dataset and a second set of rules is acquired. Both of them are integrated into a single set of rules. Fuzzy modeling is then applied to the rules, transforming them into a fuzzy model, and finally, an optimization technique is used to tune the fuzzy model's parameters. The method is applied to a medical domain problem, the cardiac arrhythmic beat classification and satisfactory results have been obtained. The method experiences several advantages compared to approaches based solely on expert's knowledge or mined knowledge while the ability to interpret the decisions made from the created fuzzy expert system is a major advantage compared to "black box" approaches.

  1. Capacity planning for waste management systems: an interval fuzzy robust dynamic programming approach.

    PubMed

    Nie, Xianghui; Huang, Guo H; Li, Yongping

    2009-11-01

    This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem. Consequently, robustness of the optimization process and solution can be enhanced. The modeling approach is applied to a hypothetical problem for the planning of waste-flow allocation and treatment/disposal facility expansion within a municipal solid waste (MSW) management system. Interval solutions for capacity expansion of waste management facilities and relevant waste-flow allocation are generated and interpreted to provide useful decision alternatives. The results indicate that robust and useful solutions can be obtained, and the proposed IFRDP approach is applicable to practical problems that are associated with highly complex and uncertain information.

  2. Search and selection hotel system in Surabaya based on geographic information system (GIS) with fuzzy logic

    NASA Astrophysics Data System (ADS)

    Purbandini, Taufik

    2016-03-01

    Surabaya is a metropolitan city in Indonesia. When the visitor has an interest in Surabaya for several days, then the visitor was looking for lodging that is closest to the interests of making it more efficient and practical. It was not a waste of time for the businessman because of congestion and so we need full information about the hotel as an inn during a stay in Surabaya began name, address of the hotel, the hotel's website, the distance from the hotel to the destination until the display of the map along the route with the help of Google Maps. This system was designed using fuzzy logic which aims to assist the user in making decisions. Design of hotel search and selection system was done through four stages. The first phase was the collection of data and as the factors that influence the decision-making along with the limit values of these factors. Factors that influence covers a distance of the hotel, the price of hotel rooms, and hotel reviews. The second stage was the processing of data and information by creating membership functions. The third stage was the analysis of systems with fuzzy logic. The steps were performed in systems analysis, namely fuzzification, inference using Mamdani, and defuzzification. The last stage was the design and construction of the system. Designing the system using use case diagrams and activity diagram to describe any process that occurs. Development system includes system implementation and evaluation systems. Implementation of mobile with Android-based system so that these applications were user friendly.

  3. Non-monotonic robust H2 fuzzy observer-based control for discrete time nonlinear systems with parametric uncertainties

    NASA Astrophysics Data System (ADS)

    Fakhimi Derakhshan, Siavash; Fatehi, Alireza

    2015-09-01

    A non-monotonic Lyapunov function (NMLF) is deployed to design a robust H2 fuzzy observer-based control problem for discrete-time nonlinear systems in the presence of parametric uncertainties. The uncertain nonlinear system is presented as a Takagi and Sugeno (T-S) fuzzy model with norm-bounded uncertainties. The states of the fuzzy system are estimated by a fuzzy observer and the control design is established based on a parallel distributed compensation scheme. In order to derive a sufficient condition to establish the global asymptotic stability of the proposed closed-loop fuzzy system, an NMLF is adopted and an upper bound on the quadratic cost function is provided. The existence of a robust H2 fuzzy observer-based controller is expressed as a sufficient condition in the form of linear matrix inequalities (LMIs) and a sub-optimal fuzzy observer-based controller in the sense of cost bound minimization is obtained by utilising the aforementioned LMI optimisation techniques. Finally, the effectiveness of the proposed scheme is shown through an example.

  4. Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

    PubMed

    Kaya, Mehmet; Alhajj, Reda

    2005-04-01

    Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.

  5. Disturbance observer based fault estimation and dynamic output feedback fault tolerant control for fuzzy systems with local nonlinear models.

    PubMed

    Han, Jian; Zhang, Huaguang; Wang, Yingchun; Liu, Yang

    2015-11-01

    This paper addresses the problems of fault estimation (FE) and fault tolerant control (FTC) for fuzzy systems with local nonlinear models, external disturbances, sensor and actuator faults, simultaneously. Disturbance observer (DO) and FE observer are designed, simultaneously. Compared with the existing results, the proposed observer is with a wider application range. Using the estimation information, a novel fuzzy dynamic output feedback fault tolerant controller (DOFFTC) is designed. The controller can be used for the fuzzy systems with unmeasurable local nonlinear models, mismatched input disturbances, and measurement output affecting by sensor faults and disturbances. At last, the simulation shows the effectiveness of the proposed methods.

  6. Finite-time fuzzy stabilisation and control for nonlinear descriptor systems with non-zero initial state

    NASA Astrophysics Data System (ADS)

    Su, Zhan; Zhang, Qingling; Ai, Jun; Sun, Xin

    2015-01-01

    For nonlinear descriptor systems, this paper presents an approach to obtain a fuzzy controller with guaranteed finite-time stability and finite-time boundedness with non-zero initial state, which outperforms some recent work and additionally provides a precision estimation of model approximation. We prove necessary and sufficient conditions of finite-time stability and finite-time boundedness with non-zero initial state for nonlinear descriptor systems. Using Takagi-Sugeno fuzzy dynamic models and proposed sufficient conditions, we define fuzzy sets and use linear matrix inequalities to satisfy differential linear matrix inequalities. A simulation confirms efficiency and precision of the given method.

  7. A Fuzzy Feed-Forward/Feedback Control System for a Three-Phase Oil Field Centrifuge.

    SciTech Connect

    Parkinson, W. J. ,; Smith, R. E.; Mortensen, F. N.; Wantuck, P. J.; Ross, Timothy J.; Jamshidi, Mohammad; Miller, N.

    2002-01-01

    A set of fuzzy controllers was designed and applied to a commercial three-phase oil field centrifuge. This centrifuge is essentially a one of a kind unit. It is used to recover oil from tank bottoms and oil field and/or refinery sludge. It is unique because it can separate oily emulsions into three separate phases, oil, water, and solids, in one operation. The centrifuge is a large but portable device. It is moved form site to site and is used to separate a large variety of waste emulsions. The centrifuge feedstock varies significantly from site to site and often varies significantly during the daily operation. In this application, fuzzy logic was used on a class of problems not easily solved by classical control techniques. The oil field centrifuge is a highly nonlinear system, with a time varying input. We have been unable to develop a physical-mathematical model of the portion of the centrifuge operation that actually separates the oil, water, and solids. For this portion of the operation we developed a fuzzy feedback control system that modeled a skilled operator's knowledge and actions as opposed to the physical model of the centrifuge itself. Because of the variable feed we had to develop a feed-forward controller that would sense and react to feed changes prior to the time that the actual change reached the centrifuge separation unit. This portion of the control system was also a fuzzy controller designed around the knowledge of a skilled operator. In addition to the combined feed-forward and feedback control systems, we developed a soft-sensor that was used to determine the value of variables needed for the feed-forward control system. These variables could not actually be measured but were calculated from the measurement of other variables. The soft-sensor was developed with a combination of a physical model of the feed system and a skilled operator's expert knowledge. Finally the entire control system is tied together with a fuzzy-SPC (Statistical Process

  8. 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.

  9. Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

    SciTech Connect

    Miltiadis Alamaniotis; Vivek Agarwal

    2014-10-01

    This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

  10. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

    PubMed Central

    Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

    2016-01-01

    Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

  11. Fuzzy comprehensive evaluation for grid-connected performance of integrated distributed PV-ES systems

    NASA Astrophysics Data System (ADS)

    Lv, Z. H.; Li, Q.; Huang, R. W.; Liu, H. M.; Liu, D.

    2016-08-01

    Based on the discussion about topology structure of integrated distributed photovoltaic (PV) power generation system and energy storage (ES) in single or mixed type, this paper focuses on analyzing grid-connected performance of integrated distributed photovoltaic and energy storage (PV-ES) systems, and proposes a comprehensive evaluation index system. Then a multi-level fuzzy comprehensive evaluation method based on grey correlation degree is proposed, and the calculations for weight matrix and fuzzy matrix are presented step by step. Finally, a distributed integrated PV-ES power generation system connected to a 380 V low voltage distribution network is taken as the example, and some suggestions are made based on the evaluation results.

  12. Intelligent Fuzzy Optimal Active and Combinatorial Control System of Building Structures

    NASA Astrophysics Data System (ADS)

    Tani, Akinori; Tanaka, Kenji; Yamabe, Yuichiro; Kawamura, Hiroshi

    The authors have already proposed an intelligent fuzzy optimal and active control system (IFOACS) and the effectiveness of IFOACS was proved using digital simulations and shaking table tests. However, the results show that the control effect of IFOACS becomes small in case of near-source region earthquakes. To improve control effects in case of near-source region earthquakes, a combinatorial control system (CCS), in which IFOACS is combined with a fuzzy active control system (FACS), is also proposed. In this paper, control rules in CCS are optimized using parameter-free genetic algorithms (PfGAs) considering limitations of an actuator such as maximal strokes and control forces. Effectiveness of proposed combinatorial control system (CCS) is verified and discussed based on results of digital simulations.

  13. Maximum Energy Extraction Control for Wind Power Generation Systems Based on the Fuzzy Controller

    NASA Astrophysics Data System (ADS)

    Kamal, Elkhatib; Aitouche, Abdel; Mohammed, Walaa; Sobaih, Abdel Azim

    2016-10-01

    This paper presents a robust controller for a variable speed wind turbine with a squirrel cage induction generator (SCIG). For variable speed wind energy conversion system, the maximum power point tracking (MPPT) is a very important requirement in order to maximize the efficiency. The system is nonlinear with parametric uncertainty and subject to large disturbances. A Takagi-Sugeno (TS) fuzzy logic is used to model the system dynamics. Based on the TS fuzzy model, a controller is developed for MPPT in the presence of disturbances and parametric uncertainties. The proposed technique ensures that the maximum power point (MPP) is determined, the generator speed is controlled and the closed loop system is stable. Robustness of the controller is tested via the variation of model's parameters. Simulation studies clearly indicate the robustness and efficiency of the proposed control scheme compared to other techniques.

  14. 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.

  15. Performance analysis of a semiactive suspension system with particle swarm optimization and fuzzy logic control.

    PubMed

    Qazi, Abroon Jamal; de Silva, Clarence W; Khan, Afzal; Khan, Muhammad Tahir

    2014-01-01

    This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.

  16. Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

    PubMed Central

    Qazi, Abroon Jamal; de Silva, Clarence W.

    2014-01-01

    This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868

  17. Fuzzy Identification Based on T-S Fuzzy Model and Its Application for SCR System

    NASA Astrophysics Data System (ADS)

    Zeng, Fanchun; Zhang, Bin; Zhang, Lu; Ji, Jinfu; Jin, Wenjing

    An improved T-S model was introduced to identify the model of SCR system. Model structure was selected by physical analyzes and mathematics tests. Three different clustering algorithms were introduced to obtain space partitions. Then, space partitions were amended by mathematics methods. At last, model parameters were identified by least square method. Train data was sampled in 1000MW coal-fired unit SCR system. T-S model of it is identified by three cluster methods. Identify results are proved effective. The merit and demerit among them are analyzed in the end.

  18. A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

    NASA Technical Reports Server (NTRS)

    Wu, G. Gordon

    1995-01-01

    Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.

  19. Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

    PubMed

    Hashim, H A; Abido, M A

    2015-01-01

    This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

  20. Prioritizing factors influencing nurses' satisfaction with hospital information systems: a fuzzy analytic hierarchy process approach.

    PubMed

    Kimiafar, Khalil; Sadoughi, Farahnaz; Sheikhtaheri, Abbas; Sarbaz, Masoumeh

    2014-04-01

    Our aim was to use the fuzzy analytic hierarchy process approach to prioritize the factors that influence nurses' satisfaction with a hospital information system. First, we reviewed the related literature to identify and select possible factors. Second, we developed an analytic hierarchy process framework with three main factors (quality of services, of systems, and of information) and 22 subfactors. Third, we developed a questionnaire based on pairwise comparisons and invited 10 experienced nurses who were identified through snowball sampling to rate these factors. Finally, we used Chang's fuzzy extent analysis method to compute the weights of these factors and prioritize them. We found that information quality was the most important factor (58%), followed by service quality (22%) and then system quality (19%). In conclusion, although their weights were not similar, all factors were important and should be considered in evaluating nurses' satisfaction.

  1. Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study

    PubMed Central

    Hashim, H. A.; Abido, M. A.

    2015-01-01

    This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. PMID:25960738

  2. Reliable Sampled-Data Control of Fuzzy Markovian Systems with Partly Known Transition Probabilities

    NASA Astrophysics Data System (ADS)

    Sakthivel, R.; Kaviarasan, B.; Kwon, O. M.; Rathika, M.

    2016-08-01

    This article presents a fuzzy dynamic reliable sampled-data control design for nonlinear Markovian jump systems, where the nonlinear plant is represented by a Takagi-Sugeno fuzzy model and the transition probability matrix for Markov process is permitted to be partially known. In addition, a generalised as well as more practical consideration of the real-world actuator fault model which consists of both linear and nonlinear fault terms is proposed to the above-addressed system. Then, based on the construction of an appropriate Lyapunov-Krasovskii functional and the employment of convex combination technique together with free-weighting matrices method, some sufficient conditions that promising the robust stochastic stability of system under consideration and the existence of the proposed controller are derived in terms of linear matrix inequalities, which can be easily solved by any of the available standard numerical softwares. Finally, a numerical example is provided to illustrate the validity of the proposed methodology.

  3. Fuzzy Stochastic Optimal Guaranteed Cost Control of Bio-Economic Singular Markovian Jump Systems.

    PubMed

    Li, Li; Zhang, Qingling; Zhu, Baoyan

    2015-11-01

    This paper establishes a bio-economic singular Markovian jump model by considering the price of the commodity as a Markov chain. The controller is designed for this system such that its biomass achieves the specified range with the least cost in a finite-time. Firstly, this system is described by Takagi-Sugeno fuzzy model. Secondly, a new design method of fuzzy state-feedback controllers is presented to ensure not only the regularity, nonimpulse, and stochastic singular finite-time boundedness of this kind of systems, but also an upper bound achieved for the cost function in the form of strict linear matrix inequalities. Finally, two examples including a practical example of eel seedling breeding are given to illustrate the merit and usability of the approach proposed in this paper.

  4. Stabilization loop of a two axes gimbal system using self-tuning PID type fuzzy controller.

    PubMed

    Abdo, Maher Mahmoud; Vali, Ahmad Reza; Toloei, Ali Reza; Arvan, Mohammad Reza

    2014-03-01

    The application of inertial stabilization system is to stabilize the sensor's line of sight toward a target by isolating the sensor from the disturbances induced by the operating environment. The aim of this paper is to present two axes gimbal system. The gimbals torque relationships are derived using Lagrange equation considering the base angular motion and dynamic mass unbalance. The stabilization loops are constructed with cross coupling unit utilizing proposed fuzzy PID type controller. The overall control system is simulated and validated using MATLAB. Then, the performance of proposed controller is evaluated comparing with conventional PI controller in terms of transient response analysis and quantitative study of error analysis. The simulation results obtained in different conditions prove the efficiency of the proposed fuzzy controller which offers a better response than the classical one, and improves further the transient and steady-state performance.

  5. 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.

  6. Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

    SciTech Connect

    Cikanek, Susan R.

    1994-01-01

    An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control.

  7. Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

    SciTech Connect

    Cikanek, S.R.

    1994-10-25

    An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control. 123 figs.

  8. Value Systems in International Business.

    ERIC Educational Resources Information Center

    Heiba, Farouk I.

    Every society has a system of values and seeks to achieve goals which it defines as desirable. To gain insight and a measure of understanding of another culture, international marketers can approach a country as a whole, seek out behavioral premises, obtain a theoretical knowledge of the culture, and learn the country's social heritage.…

  9. International Instructional Systems: Social Studies

    ERIC Educational Resources Information Center

    Brant, Jacek; Chapman, Arthur; Isaacs, Tina

    2016-01-01

    This paper reports on research conducted as part of the International Instructional System Study that explored five subject areas across nine jurisdictions in six high-performing countries. The Study's overall aim was to understand what, if anything, there is in common in the curricula and assessment arrangements among the high-performing…

  10. A new fuzzy self-tuning PD load frequency controller for micro-hydropower system

    NASA Astrophysics Data System (ADS)

    Reyasudin Basir Khan, M.; Jidin, Razali; Pasupuleti, Jagadeesh

    2016-03-01

    This paper presents a new approach for controlling the secondary load bank of a micro-hydropower system using a fuzzy self-tuning proportional-derivative (PD) controller. This technology is designed in order to optimize the micro-hydropower system in a resort island located in the South China Sea. Thus, this technology will be able to mitigate the diesel fuel consumption and cost of electricity supply on the island. The optimal hydropower generation for this system depends on the available stream flow at the potential sites. At low stream flow, both the micro-hydropower system and the currently installed diesel generators are required to feed the load. However, when the hydropower generation exceeds the load demand, the diesel generator is shut down. Meanwhile, the system frequency is controlled by a secondary load bank that absorbs the hydropower which exceeds the consumer demand. The fuzzy rules were designed to automatically tune the PD gains under dynamic frequency variations. Performances of the fuzzy self-tuning PD controller were compared with the conventional PD controller. The result of the controller implementation shows the viability of the proposed new controller in achieving a higher performance and more robust load frequency control than the conventional PD controller.

  11. Simulink-based HW/SW codesign of embedded neuro-fuzzy systems.

    PubMed

    Reyneri, L M; Chiaberge, M; Lavagno, L

    2000-06-01

    We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures.

  12. Do cybernetics, system science and fuzzy sets share some epistemological problems. I. An analysis of cybernetics

    SciTech Connect

    Tamburrini, G.; Termini, S.

    1982-01-01

    The general thesis underlying the present paper is that there are very strong methodological relations among cybernetics, system science, artificial intelligence, fuzzy sets and many other related fields. Then, in order to understand better both the achievements and the weak points of all the previous disciplines, one should look for some common features for looking at them in this general frame. What will be done is to present a brief analysis of the primitive program of cybernetics, presenting it as a case study useful for developing the previous thesis. Among the discussed points are the problems of interdisciplinarity and of the unity of cybernetics. Some implications of this analysis for a new reading of general system theory and fuzzy sets are briefly outlined at the end of the paper. 3 references.

  13. Fuzzy Adaptive Quantized Control for a Class of Stochastic Nonlinear Uncertain Systems.

    PubMed

    Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, C L Philip

    2016-02-01

    In this paper, a fuzzy adaptive approach for stochastic strict-feedback nonlinear systems with quantized input signal is developed. Compared with the existing research on quantized input problem, the existing works focus on quantized stabilization, while this paper considers the quantized tracking problem, which recovers stabilization as a special case. In addition, uncertain nonlinearity and the unknown stochastic disturbances are simultaneously considered in the quantized feedback control systems. By putting forward a new nonlinear decomposition of the quantized input, the relationship between the control signal and the quantized signal is established, as a result, the major technique difficulty arising from the piece-wise quantized input is overcome. Based on fuzzy logic systems' universal approximation capability, a novel fuzzy adaptive tracking controller is constructed via backstepping technique. The proposed controller guarantees that the tracking error converges to a neighborhood of the origin in the sense of probability and all the signals in the closed-loop system remain bounded in probability. Finally, an example illustrates the effectiveness of the proposed control approach.

  14. Profitability analysis of a femtosecond laser system for cataract surgery using a fuzzy logic approach

    PubMed Central

    Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M

    2016-01-01

    AIM To define the financial and management conditions required to introduce a femtosecond laser system for cataract surgery in a clinic using a fuzzy logic approach. METHODS In the simulation performed in the current study, the costs associated to the acquisition and use of a commercially available femtosecond laser platform for cataract surgery (VICTUS, TECHNOLAS Perfect Vision GmbH, Bausch & Lomb, Munich, Germany) during a period of 5y were considered. A sensitivity analysis was performed considering such costs and the countable amortization of the system during this 5y period. Furthermore, a fuzzy logic analysis was used to obtain an estimation of the money income associated to each femtosecond laser-assisted cataract surgery (G). RESULTS According to the sensitivity analysis, the femtosecond laser system under evaluation can be profitable if 1400 cataract surgeries are performed per year and if each surgery can be invoiced more than $500. In contrast, the fuzzy logic analysis confirmed that the patient had to pay more per surgery, between $661.8 and $667.4 per surgery, without considering the cost of the intraocular lens (IOL). CONCLUSION A profitability of femtosecond laser systems for cataract surgery can be obtained after a detailed financial analysis, especially in those centers with large volumes of patients. The cost of the surgery for patients should be adapted to the real flow of patients with the ability of paying a reasonable range of cost. PMID:27500115

  15. Output feedback fuzzy controller design with local nonlinear feedback laws for discrete-time nonlinear systems.

    PubMed

    Dong, Jiuxiang; Wang, Youyi; Yang, Guang-Hong

    2010-12-01

    This paper considers the output feedback control problem for nonlinear discrete-time systems, which are represented by a type of fuzzy systems with local nonlinear models. By using the estimations of the states and nonlinear functions in local models, sufficient conditions for designing observer-based controllers are given for discrete-time nonlinear systems. First, a separation property, i.e., the controller and the observer can be independently designed, is proved for the class of fuzzy systems. Second, a two-step procedure with cone complementarity linearization algorithms is also developed for solving the H( ∞) dynamic output feedback (DOF) control problem. Moreover, for the case where the nonlinear functions in local submodels are measurable, a convex condition for designing H(∞) controllers is given by a new DOF control scheme. In contrast to the existing methods, the new methods can design output feedback controllers with fewer fuzzy rules as well as less computational burden, which is helpful for controller designs and implementations. Lastly, numerical examples are given to illustrate the effectiveness of the proposed methods.

  16. Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.

    PubMed

    Trnka, Hjalte; Wu, Jian X; Van De Weert, Marco; Grohganz, Holger; Rantanen, Jukka

    2013-12-01

    Freeze-drying of peptide and protein-based pharmaceuticals is an increasingly important field of research. The diverse nature of these compounds, limited understanding of excipient functionality, and difficult-to-analyze quality attributes together with the increasing importance of the biosimilarity concept complicate the development phase of safe and cost-effective drug products. To streamline the development phase and to make high-throughput formulation screening possible, efficient solutions for analyzing critical quality attributes such as cake quality with minimal material consumption are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible.

  17. Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication.

    PubMed

    Peng, Chen; Ma, Shaodong; Xie, Xiangpeng

    2017-02-07

    This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.

  18. Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

    PubMed

    Pasquier, M; Quek, C; Toh, M

    2001-10-01

    This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

  19. Design and implementation of a new fuzzy PID controller for networked control systems.

    PubMed

    Fadaei, A; Salahshoor, K

    2008-10-01

    This paper presents a practical network platform to design and implement a networked-based cascade control system linking a Smar Foundation Fieldbus (FF) controller (DFI-302) and a Siemens programmable logic controller (PLC-S7-315-2DP) through Industrial Ethernet to a laboratory pilot plant. In the presented network configuration, the Smar OPC tag browser and Siemens WinCC OPC Channel provide the communicating interface between the two controllers. The paper investigates the performance of a PID controller implemented in two different possible configurations of FF function block (FB) and networked control system (NCS) via a remote Siemens PLC. In the FB control system implementation, the desired set-point is provided by the Siemens Human-Machine Interface (HMI) software (i.e, WinCC) via an Ethernet Modbus link. While, in the NCS implementation, the cascade loop is realized in remote Siemens PLC station and the final element set-point is sent to the Smar FF station via Ethernet bus. A new fuzzy PID control strategy is then proposed to improve the control performances of the networked-based control systems due to an induced transmission delay degradation effect. The proposed strategy utilizes an innovative idea based on sectionalizing the error signal of the step response into three different functional zones. The supporting philosophy behind these three functional zones is to decompose the desired control objectives in terms of rising time, settling time and steady-state error measures maintained by an appropriate PID-type controller in each zone. Then, fuzzy membership factors are defined to configure the control signal on the basis of the fuzzy weighted PID outputs of all three zones. The obtained results illustrate the effectiveness of the proposed fuzzy PID control scheme in improving the performances of the implemented NCS for different transportation delays.

  20. Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

    NASA Technical Reports Server (NTRS)

    Gulati, Sandeep; Toomarian, Nikzad; Barhen, Jacob; Maccalla, Ayanna; Tawel, Raoul; Thakoor, Anil; Daud, Taher

    1994-01-01

    Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.

  1. Multi-criteria optimisation problems for chemical engineering systems and algorithms for their solution based on fuzzy mathematical methods.

    PubMed

    Orazbayev, B B; Orazbayeva, K N; Kurmangaziyeva, L T; Makhatova, V E

    2015-01-01

    Mathematical equations for the multi-criteria task of the optimisation of chemical engineering systems, for example for the optimisation of working regimes for industrial installations for benzene production, have been formulated and developed, and based on fuzzy mathematical methods, algorithms for their solution have been developed. Since the chemical engineering system, which is being researched, is characterised by multiple criteria and often functions in conditions of uncertainty, the presenting problem is formulated in the form of multi-criteria equations for fuzzy mathematical programming. New mathematical formulations for the problems being solved in a fuzzy environment and heuristic algorithms for their solution have been developed by the modification of various optimisation principles based on fuzzy mathematical methods.

  2. Multi-criteria optimisation problems for chemical engineering systems and algorithms for their solution based on fuzzy mathematical methods

    PubMed Central

    Orazbayev, B. B.; Orazbayeva, K. N.; Kurmangaziyeva, L. T.; Makhatova, V.E.

    2015-01-01

    Mathematical equations for the multi-criteria task of the optimisation of chemical engineering systems, for example for the optimisation of working regimes for industrial installations for benzene production, have been formulated and developed, and based on fuzzy mathematical methods, algorithms for their solution have been developed. Since the chemical engineering system, which is being researched, is characterised by multiple criteria and often functions in conditions of uncertainty, the presenting problem is formulated in the form of multi-criteria equations for fuzzy mathematical programming. New mathematical formulations for the problems being solved in a fuzzy environment and heuristic algorithms for their solution have been developed by the modification of various optimisation principles based on fuzzy mathematical methods. PMID:28275318

  3. 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.

  4. An Extended Membrane System with Active Membranes to Solve Automatic Fuzzy Clustering Problems.

    PubMed

    Peng, Hong; Wang, Jun; Shi, Peng; Pérez-Jiménez, Mario J; Riscos-Núñez, Agustín

    2016-05-01

    This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object's evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.

  5. Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.

    PubMed

    Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir

    2015-01-01

    Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems.

  6. Type II fuzzy systems for amyloid plaque segmentation in transgenic mouse brains for Alzheimer's disease quantification

    NASA Astrophysics Data System (ADS)

    Khademi, April; Hosseinzadeh, Danoush

    2014-03-01

    Alzheimer's disease (AD) is the most common form of dementia in the elderly characterized by extracellular deposition of amyloid plaques (AP). Using animal models, AP loads have been manually measured from histological specimens to understand disease etiology, as well as response to treatment. Due to the manual nature of these approaches, obtaining the AP load is labourious, subjective and error prone. Automated algorithms can be designed to alleviate these challenges by objectively segmenting AP. In this paper, we focus on the development of a novel algorithm for AP segmentation based on robust preprocessing and a Type II fuzzy system. Type II fuzzy systems are much more advantageous over the traditional Type I fuzzy systems, since ambiguity in the membership function may be modeled and exploited to generate excellent segmentation results. The ambiguity in the membership function is defined as an adaptively changing parameter that is tuned based on the local contrast characteristics of the image. Using transgenic mouse brains with AP ground truth, validation studies were carried out showing a high degree of overlap and low degree of oversegmentation (0.8233 and 0.0917, respectively). The results highlight that such a framework is able to handle plaques of various types (diffuse, punctate), plaques with varying Aβ concentrations as well as intensity variation caused by treatment effects or staining variability.

  7. Adaptive fuzzy control with output feedback for H infinity tracking of SISO nonlinear systems.

    PubMed

    Rigatos, Gerasimos G

    2008-08-01

    Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the proposed controller are (i) to transform the nonlinear control problem into a regulation problem through suitable output feedback, (ii) to design a state observer for the estimation of the non-measurable elements of the system's state vector, (iii) to design neuro-fuzzy approximators that receive as inputs the parameters of the reconstructed state vector and give as output an estimation of the system's unknown dynamics, (iv) to use an H(infinity) control term for the compensation of external disturbances and modelling errors, (v) to use Lyapunov stability analysis in order to find the learning law for the neuro-fuzzy approximators, and a supervisory control term for disturbance and modelling error rejection. The control scheme is tested in the cart-pole balancing problem and in a DC-motor model.

  8. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

    PubMed

    Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás

    2015-01-01

    This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  9. Optimization of regional economic and environmental systems under fuzzy and random uncertainties.

    PubMed

    Li, Y P; Huang, G H; Nie, S L

    2011-08-01

    Environmental problems associated with socio-economic development have been growing concerns faced by many regional and/or national authorities. However, effective planning may encounter difficulties since uncertainties existing in a number of impact factors and pollution-related processes are often not well acknowledged and reflected. This study advances an interval-fuzzy chance-constrained programming (IFCP) method for planning regional economic and environmental systems, where uncertainties presented as intervals, fuzzy sets and probability distributions can be tackled. The developed method is applied to a real-world case for economic and environmental planning in the New Binhai District in the Municipality of Tianjin, China. Two scenarios based on multiple environmental constraints are examined. The results can help identify desired alternatives for planning regional development strategies, where compromised schemes are provided under an integrated consideration of economic efficiency and environmental protection under multiple uncertainties.

  10. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    PubMed

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

  11. Implementation of fuzzy-sliding mode based control of a grid connected photovoltaic system.

    PubMed

    Menadi, Abdelkrim; Abdeddaim, Sabrina; Ghamri, Ahmed; Betka, Achour

    2015-09-01

    The present work describes an optimal operation of a small scale photovoltaic system connected to a micro-grid, based on both sliding mode and fuzzy logic control. Real time implementation is done through a dSPACE 1104 single board, controlling a boost chopper on the PV array side and a voltage source inverter (VSI) on the grid side. The sliding mode controller tracks permanently the maximum power of the PV array regardless of atmospheric condition variations, while The fuzzy logic controller (FLC) regulates the DC-link voltage, and ensures via current control of the VSI a quasi-total transit of the extracted PV power to the grid under a unity power factor operation. Simulation results, carried out via Matlab-Simulink package were approved through experiment, showing the effectiveness of the proposed control techniques.

  12. On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems

    NASA Technical Reports Server (NTRS)

    Tunstel, Edward; Jamshidi, Mo

    1997-01-01

    Intelligent control of complex multi-variable systems can be a challenge for single fuzzy rule-based controllers. This class of problems cam often be managed with less difficulty by distributing intelligent decision-making amongst a collection of rule-bases. Such an approach requires that a mechanism be chosen to ensure goal-oriented interaction between the multiple rule-bases. In this paper, a hierarchical rule-based approach is described. Decision-making mechanisms based on generalized concepts from single-rule-based fuzzy control are described. Finally, the effects of different aggregation operators on multi-rule-base decision-making are examined in a navigation control problem for mobile robots.

  13. Fault tolerant synchronization of chaotic heavy symmetric gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control.

    PubMed

    Farivar, Faezeh; Shoorehdeli, Mahdi Aliyari

    2012-01-01

    In this paper, fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated. Taking the general nature of faults in the slave system into account, a new synchronization scheme, namely, fault tolerant synchronization, is proposed, by which the synchronization can be achieved no matter whether the faults and disturbances occur or not. By making use of a slave observer and a Lyapunov rule-based fuzzy control, fault tolerant synchronization can be achieved. Two techniques are considered as control methods: classic Lyapunov-based control and Lyapunov rule-based fuzzy control. On the basis of Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are obtained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. Two proposed methods are compared. The Lyapunov rule-based fuzzy control can compensate for the actuator faults and disturbances occurring in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method for fault tolerant synchronization.

  14. A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features.

    PubMed

    Bommanna Raja, K; Madheswaran, M; Thyagarajah, K

    2008-02-01

    The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.

  15. Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system.

    PubMed

    El-Bardini, Mohammad; El-Nagar, Ahmad M

    2014-05-01

    In this paper, the interval type-2 fuzzy proportional-integral-derivative controller (IT2F-PID) is proposed for controlling an inverted pendulum on a cart system with an uncertain model. The proposed controller is designed using a new method of type-reduction that we have proposed, which is called the simplified type-reduction method. The proposed IT2F-PID controller is able to handle the effect of structure uncertainties due to the structure of the interval type-2 fuzzy logic system (IT2-FLS). The results of the proposed IT2F-PID controller using a new method of type-reduction are compared with the other proposed IT2F-PID controller using the uncertainty bound method and the type-1 fuzzy PID controller (T1F-PID). The simulation and practical results show that the performance of the proposed controller is significantly improved compared with the T1F-PID controller.

  16. Urban area mapping from polarimetric SAR data using fuzzy inference system

    NASA Astrophysics Data System (ADS)

    Ahluwalia, Asmeet; Manickam, Surendar; Bhattacharya, Avik; Porwal, Alok

    2016-05-01

    In this work, we present urban area mapping from full-polarimetric synthetic aperture radar (SAR) data using fuzzy inference system (FIS). In particular, our aim is to utilize the profound knowledge available about scattering mechanism from urban targets to delineate urban environment. In this approach, we have utilized the recently developed polarimetric SAR scattering power decomposition technique (SD-Y4O) given in Bhattacharya et. al. The improved powers along with some other polarimetric parameters were used in this study. A suitable normalization procedure was adapted to handle the skewness in the estimated parameters. The fuzzy if-then rules were constructed from the in-depth knowledge of scattering mechanisms from an urban environment. Suitable methods were introduced to define the fuzzy inference system. The defuzzified membership values were thresholded using an unsupervised clustering method (k-means). The pixels lying in the range [μmax-σ, μmax+σ] corresponds to urban areas where µmax is the largest cluster center and σ is the standard deviation of the cluster corresponding to µmax. The extracted urban area is in visually good agreement with the high resolution optical image. ALOS PALSAR full-polarimetric L-band SAR data has been used in this study.

  17. Fuzzy logic and image processing techniques for the interpretation of seismic data

    NASA Astrophysics Data System (ADS)

    Orozco-del-Castillo, M. G.; Ortiz-Alemán, C.; Urrutia-Fucugauchi, J.; Rodríguez-Castellanos, A.

    2011-06-01

    Since interpretation of seismic data is usually a tedious and repetitive task, the ability to do so automatically or semi-automatically has become an important objective of recent research. We believe that the vagueness and uncertainty in the interpretation process makes fuzzy logic an appropriate tool to deal with seismic data. In this work we developed a semi-automated fuzzy inference system to detect the internal architecture of a mass transport complex (MTC) in seismic images. We propose that the observed characteristics of a MTC can be expressed as fuzzy if-then rules consisting of linguistic values associated with fuzzy membership functions. The constructions of the fuzzy inference system and various image processing techniques are presented. We conclude that this is a well-suited problem for fuzzy logic since the application of the proposed methodology yields a semi-automatically interpreted MTC which closely resembles the MTC from expert manual interpretation.

  18. Intelligent control of a multi-degree-of freedom reaction compensating platform system using fuzzy logic

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin B.; Lawrence, Charles; Lin, Yueh-Jaw

    1994-01-01

    This paper presents the development of a general-purpose fuzzy logic (FL) control methodology for isolating the external vibratory disturbances of space-based devices. According to the desired performance specifications, a full investigation regarding the development of an FL controller was done using different scenarios, such as variances of passive reaction-compensating components and external disturbance load. It was shown that the proposed FL controller is robust in that the FL-controlled system closely follows the prespecified ideal reference model. The comparative study also reveals that the FL-controlled system achieves significant improvement in reducing vibrations over passive systems.

  19. A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood

    PubMed Central

    Dragović, Ivana; Turajlić, Nina; Pilčević, Dejan; Petrović, Bratislav; Radojević, Dragan

    2015-01-01

    Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand. PMID:27069500

  20. An Electromyographic-driven Musculoskeletal Torque Model using Neuro-Fuzzy System Identification: A Case Study.

    PubMed

    Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza

    2014-10-01

    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.

  1. SVR learning-based spatiotemporal fuzzy logic controller for nonlinear spatially distributed dynamic systems.

    PubMed

    Zhang, Xian-Xia; Jiang, Ye; Li, Han-Xiong; Li, Shao-Yuan

    2013-10-01

    A data-driven 3-D fuzzy-logic controller (3-D FLC) design methodology based on support vector regression (SVR) learning is developed for nonlinear spatially distributed dynamic systems. Initially, the spatial information expression and processing as well as the fuzzy linguistic expression and rule inference of a 3-D FLC are integrated into spatial fuzzy basis functions (SFBFs), and then the 3-D FLC can be depicted by a three-layer network structure. By relating SFBFs of the 3-D FLC directly to spatial kernel functions of an SVR, an equivalence relationship of the 3-D FLC and the SVR is established, which means that the 3-D FLC can be designed with the help of the SVR learning. Subsequently, for an easy implementation, a systematic SVR learning-based 3-D FLC design scheme is formulated. In addition, the universal approximation capability of the proposed 3-D FLC is presented. Finally, the control of a nonlinear catalytic packed-bed reactor is considered as an application to demonstrate the effectiveness of the proposed 3-D FLC.

  2. Modeling gunshot bruises in soft body armor with an adaptive fuzzy system.

    PubMed

    Lee, Ian; Kosko, Bart; Anderson, W French

    2005-12-01

    Gunshots produce bruise patterns on persons who wear soft body armor when shot even though the armor stops the bullets. An adaptive fuzzy system modeled these bruise patterns based on the depth and width of the deformed armor given a projectile's mass and momentum. The fuzzy system used rules with sinc-shaped if-part fuzzy sets and was robust against random rule pruning: Median and mean test errors remained low even after removing up to one fifth of the rules. Handguns shot different caliber bullets at armor that had a 10%-ordnance gelatin backing. The gelatin blocks were tissue simulants. The gunshot data tuned the additive fuzzy function approximator. The fuzzy system's conditional variance V[Y/X = x] described the second-order uncertainty of the function approximation. Handguns with different barrel lengths shot bullets over a fixed distance at armor-clad gelatin blocks that we made with Type 250 A Ordnance Gelatin. The bullet-armor experiments found that a bullet's weight and momentum correlated with the depth of its impact on armor-clad gelatin (R2 = 0.881 and p-value < 0.001 for the null hypothesis that the regression line had zero slope). Related experiments on plumber's putty showed that highspeed baseball impacts compared well to bullet-armor impacts for large-caliber handguns. A baseball's momentum correlated with its impact depth in putty (R2 = 0.93 and p-value < 0.001). A bullet's momentum similarly correlated with its armor-impact in putty (R2 = 0.97 and p-value < 0.001). A Gujarati-Chow test showed that the two putty-impact regression lines had statistically indistinguishable slopes for p-value = 0.396. Baseball impact depths were comparable to bullet-armor impact depths: Getting shot with a .22 caliber bullet when wearing soft body armor resembles getting hit in the chest with a 40-mph baseball. Getting shot with a .45 caliber bullet resembles getting hit with a 90-mph baseball.

  3. Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.

    PubMed

    Huq, Rajibul; Wang, Rosalie; Lu, Elaine; Hebert, Debbie; Lacheray, Hervé; Mihailidis, Alex

    2013-06-01

    This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.

  4. A transductive neuro-fuzzy controller: application to a drilling process.

    PubMed

    Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R

    2010-07-01

    Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage.

  5. Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system.

    PubMed

    Das, Suddhasattwa; Roy Chowdhury, Shubhajit; Saha, Hiranmay

    2012-06-01

    The paper attempts to improve the accuracy of a fuzzy expert decision making system by tuning the parameters of type-2 sigmoid membership functions of fuzzy input variables and hence determining the most appropriate type-1 membership function. The current work mathematically models the variability of human decision making process using type-2 fuzzy sets. Moreover, an index of accuracy of a fuzzy expert system has been proposed and determined analytically. It has also been ascertained that there exists only one rule in the rule base whose associated mapping for the ith linguistic variable maps to the same value as the maximum value of the membership function for the ith linguistic variable. The improvement in decision making accuracy was successfully verified in a medical diagnostic decision making system for renal diagnostic applications. Based on the accuracy estimations applied over a set of pathophysiological parameters, viz. body mass index, glucose, urea, creatinine, systolic and diastolic blood pressure, appropriate type-1 fuzzy sets of these parameters have been determined assuming normal distribution of type-1 membership function values in type-2 fuzzy sets. The type-1 fuzzy sets so determined have been used to develop an FPGA based smart processor. Using the processor, renal diagnosis of patients has been performed with an accuracy of 98.75%.

  6. Neuro-fuzzy identification applied to fault detection in nonlinear systems

    NASA Astrophysics Data System (ADS)

    Blázquez, L. Felipe; de Miguel, Luis J.; Aller, Fernando; Perán, José R.

    2011-10-01

    This article describes a fault detection method, based on the parity equations approach, to be applied to nonlinear systems. The input-output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference architecture and its learning algorithm. The proposed method is able to detect small abrupt faults, even in systems with unknown nonlinearities. This method has been applied to a real industrial pilot plant, and good performance has been obtained for the experimental case of fault detection in the level sensor of a level control process in the said industrial pilot plant.

  7. Fuzzy Subspace Clustering

    NASA Astrophysics Data System (ADS)

    Borgelt, Christian

    In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

  8. Fuzzy Logic and Glacier Dynamics Assessment: New Paradigms for Operational Hazard Detection Systems

    NASA Astrophysics Data System (ADS)

    Furfaro, R.; Kargel, J. S.; Fink, W.; Bishop, M. P.

    2008-12-01

    Satellite-based remote sensing is critical for monitoring highly dynamic environments that include rapidly changing alpine glaciers, melt-water production, and a variety of natural hazards. Multi-spectral and multi- temporal satellite data in conjunction with digital elevation models can be utilized to assess supraglacial and proglacial lakes, valley impoundment water volumes, and the potential for flood and debris-flow hazards. Advanced remote sensing and GIS-based methodologies represent the only effective approach for periodic assessment and detection of glacier hazards using spatio-temporal data and analysis. Such approaches, however, do not address all of the requirements needed for the development of hazard/disaster warning systems and the generation of unique information to help establish mitigation strategies. Consequently, our objectives are to introduce the methods of fuzzy logic as an additional level of analysis and interpretation to demonstrate how intelligent, knowledge-driven algorithms can be used to assess glacier dynamics and glacier-induced hazards. Operational monitoring of dynamic environments and natural hazards will require multiple levels of analysis and information production using on-board automation. These systems must autonomously assess the hazard potential related to surface processes and the topography, while being able to identify disaster conditions. Such systems should (1) include analytical capabilities to permit automated and comprehensive identification, characterization, and quantification of terrain features (e.g., via Automated Global Feature Analyzer "AGFA"); (2) permit operational multi-scale hazard potential assessment (e.g., automatic global, regional and local assessment capabilities); and (3) permit data integration that fuses existing data and real-time data acquisition into a spatio-temporal framework that facilitates intelligent assessment and monitoring. The fuzzy logic framework may be an ideal approach that serves

  9. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    PubMed

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  10. Fuzzy chaos control for vehicle lateral dynamics based on active suspension system

    NASA Astrophysics Data System (ADS)

    Huang, Chen; Chen, Long; Jiang, Haobin; Yuan, Chaochun; Xia, Tian

    2014-07-01

    The existing research of the active suspension system (ASS) mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.

  11. Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges

    NASA Astrophysics Data System (ADS)

    Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu

    2016-09-01

    In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.

  12. Competitive coevolutionary learning of fuzzy systems for job exchange in computational grids.

    PubMed

    Fölling, Alexander; Grimme, Christian; Lepping, Joachim; Papaspyrou, Alexander; Schwiegelshohn, Uwe

    2009-01-01

    In our work, we address the problem of workload distribution within a computational grid. In this scenario, users submit jobs to local high performance computing (HPC) systems which are, in turn, interconnected such that the exchange of jobs to other sites becomes possible. Providers are able to avoid local execution of jobs by offering them to other HPC sites. In our implementation, this distribution decision is made by a fuzzy system controller whose parameters can be adjusted to establish different exchange behaviors. In such a system, it is essential that HPC sites can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. However, each site egoistically strives only for the minimization of its own jobs' response times regularly at the expense of other sites. This scenario is particularly suited for the application of a competitive coevolutionary algorithm: the fuzzy systems of the participating HPC sites are modeled as species that evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and grid setups, we show that opportunistic cooperation leads to significant improvements for each HPC site as well as for the overall system.

  13. Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process.

    PubMed

    Pires, O C; Palma, C; Costa, J C; Moita, I; Alves, M M; Ferreira, E C

    2006-01-01

    A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.

  14. Computer-aided bone age assessment for ethnically diverse older children using integrated fuzzy logic system

    NASA Astrophysics Data System (ADS)

    Ma, Kevin; Moin, Paymann; Zhang, Aifeng; Liu, Brent

    2010-03-01

    Bone Age Assessment (BAA) of children is a clinical procedure frequently performed in pediatric radiology to evaluate the stage of skeletal maturation based on the left hand x-ray radiograph. The current BAA standard in the US is using the Greulich & Pyle (G&P) Hand Atlas, which was developed fifty years ago and was only based on Caucasian population from the Midwest US. To bring the BAA procedure up-to-date with today's population, a Digital Hand Atlas (DHA) consisting of 1400 hand images of normal children of different ethnicities, age, and gender. Based on the DHA and to solve inter- and intra-observer reading discrepancies, an automatic computer-aided bone age assessment system has been developed and tested in clinical environments. The algorithm utilizes features extracted from three regions of interests: phalanges, carpal, and radius. The features are aggregated into a fuzzy logic system, which outputs the calculated bone age. The previous BAA system only uses features from phalanges and carpal, thus BAA result for children over age of 15 is less accurate. In this project, the new radius features are incorporated into the overall BAA system. The bone age results, calculated from the new fuzzy logic system, are compared against radiologists' readings based on G&P atlas, and exhibits an improvement in reading accuracy for older children.

  15. ? and ? nonquadratic stabilisation of discrete-time Takagi-Sugeno systems based on multi-instant fuzzy Lyapunov functions

    NASA Astrophysics Data System (ADS)

    Tognetti, Eduardo S.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.

    2015-01-01

    The problem of state feedback control design for discrete-time Takagi-Sugeno (TS) (T-S) fuzzy systems is investigated in this paper. A Lyapunov function, which is quadratic in the state and presents a multi-polynomial dependence on the fuzzy weighting functions at the current and past instants of time, is proposed.This function contains, as particular cases, other previous Lyapunov functions already used in the literature, being able to provide less conservative conditions of control design for TS fuzzy systems. The structure of the proposed Lyapunov function also motivates the design of a new stabilising compensator for Takagi-Sugeno fuzzy systems. The main novelty of the proposed state feedback control law is that the gain is composed of matrices with multi-polynomial dependence on the fuzzy weighting functions at a set of past instants of time, including the current one. The conditions for the existence of a stabilising state feedback control law that minimises an upper bound to the ? or ? norms are given in terms of linear matrix inequalities. Numerical examples show that the approach can be less conservative and more efficient than other methods available in the literature.

  16. Genetic algorithm optimized rainfall-runoff fuzzy inference system for row crop watersheds with claypan soils

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The fuzzy logic algorithm has the ability to describe knowledge in a descriptive human-like manner in the form of simple rules using linguistic variables, and provides a new way of modeling uncertain or naturally fuzzy hydrological processes like non-linear rainfall-runoff relationships. Fuzzy infe...

  17. Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: A proof of concept study.

    PubMed

    Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam

    2016-05-01

    Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations.

  18. Disturbance Observer-Based Fuzzy Control of Uncertain MIMO Mechanical Systems With Input Nonlinearities and its Application to Robotic Exoskeleton.

    PubMed

    Chen, Ziting; Li, Zhijun; Chen, C L Philip

    2016-03-16

    We develop a novel disturbance observer-based adaptive fuzzy control approach in this paper for a class of uncertain multi-input-multi-output mechanical systems possessing unknown input nonlinearities, i.e., deadzone and saturation and time-varying external disturbance. It is shown that the input nonlinearities can be represented by a nominal part and a nonlinear disturbance term. High-dimensional integral-type Lyapunov function is used to construct the controller. Fuzzy logic system is employed to cancel model uncertainties, and disturbance observer is also integrated into control design to compensate the fuzzy approximation error, external disturbance, and nonlinear disturbance caused by the unknown input nonlinearities. Semiglobally uniformly ultimately boundness of the closed-loop control system is guaranteed with tracking errors keeping bounded. Experimental studies on a robotic exoskeleton using the proposed control demonstrate the effectiveness of the approach.

  19. Fault Estimation for Fuzzy Delay Systems: A Minimum Norm Least Squares Solution Approach.

    PubMed

    Huang, Sheng-Juan; Yang, Guang-Hong

    2016-07-18

    This paper mainly focuses on the problem of fault estimation for a class of Takagi-Sugeno fuzzy systems with state delays. A minimum norm least squares solution (MNLSS) approach is first introduced to establish a fault estimation compensator, which is able to optimize the fault estimator. Compared with most of the existing fault estimation methods, the MNLSS-based fault estimation method can effectively decrease the effect of state errors on the accuracy of fault estimation. Finally, three examples are given to illustrate the effectiveness and merits of the proposed method.

  20. Event-triggered reliable control for fuzzy Markovian jump systems with mismatched membership functions.

    PubMed

    Hou, Liyuan; Cheng, Jun; Qi, Wenhai

    2017-01-01

    The problem of event-triggered reliable control for fuzzy Markovian jump system (FMJS) with mismatched membership functions (MMFs) is addressed. Based on the mode-dependent reliable control and event-triggered communication scheme, the stability conditions and control design procedure are formulated. More precisely, a general actuator-failure is designed such that the FMJS is reliable in the sense of stochastically stable and reduce the utilization of network resources. Furthermore, the improved MMFs are introduced to reduce the conservativeness of obtained results. Finally, simulation results indicate the effectiveness of the proposed methodology.

  1. A fuzzy discrete harmony search algorithm applied to annual cost reduction in radial distribution systems

    NASA Astrophysics Data System (ADS)

    Ameli, Kazem; Alfi, Alireza; Aghaebrahimi, Mohammadreza

    2016-09-01

    Similarly to other optimization algorithms, harmony search (HS) is quite sensitive to the tuning parameters. Several variants of the HS algorithm have been developed to decrease the parameter-dependency character of HS. This article proposes a novel version of the discrete harmony search (DHS) algorithm, namely fuzzy discrete harmony search (FDHS), for optimizing capacitor placement in distribution systems. In the FDHS, a fuzzy system is employed to dynamically adjust two parameter values, i.e. harmony memory considering rate and pitch adjusting rate, with respect to normalized mean fitness of the harmony memory. The key aspect of FDHS is that it needs substantially fewer iterations to reach convergence in comparison with classical discrete harmony search (CDHS). To the authors' knowledge, this is the first application of DHS to specify appropriate capacitor locations and their best amounts in the distribution systems. Simulations are provided for 10-, 34-, 85- and 141-bus distribution systems using CDHS and FDHS. The results show the effectiveness of FDHS over previous related studies.

  2. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    PubMed Central

    Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  3. Use of fuzzy inference system for condition monitoring of induction motor

    NASA Astrophysics Data System (ADS)

    Janier, Josefina B.; Zaim Zaharia, M. F.; Karim, Samsul Ariffin Abd.

    2012-09-01

    Three phase induction motors are commonly used in industry due to its robustness, simplicity of its construction and high reliability. The tasks performed by these motors grow increasingly complex because of modern industries hence there is a need to determine the faults. Early detection of faults will reduce an unscheduled machine downtime that can upset production deadlines and may cause heavy financial losses. This paper is focused in developing a computer based system using Fuzzy Inference system's membership function. An unusual increase in vibration of the motor could be an indicator of faulty condition hence the vibration of the motor of an induction motor was used as an input, whereas the output is the motor condition. An inference system of the Fuzzy Logic was created to classify the vibration characteristics of the motor which is called vibration analysis. The system classified the motor of the gas distribution pump condition as from 'acceptable' to 'monitor closely'. The early detection of unusual increase in vibration of the induction motor is an important part of a predictive maintenance for motor driven machinery.

  4. 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

  5. Preliminary design of a SIMO fuzzy controller for steering microparticles inside blood vessels by using a magnetic resonance imaging system.

    PubMed

    Peng, Ke; Martel, Sylvain

    2011-01-01

    In this paper, a Single-Input-Multiple-Output (SIMO) fuzzy controller is designed to drive an upgraded clinical real-time Magnetic Resonance Imaging (MRI) system to provide steering forces for an aggregation of ferromagnetic microparticles in the human cardiovascular system according to a pre-set pathway. This kind of endovascular navigation is considered as an important procedure of the catheter-based method for medical treatments against diseases such as some particular types of cancers. The validity of the fuzzy controller has been tested by preliminary simulation results.

  6. Pneumatic active suspension system for a one-wheel car model using fuzzy reasoning and a disturbance observer.

    PubMed

    Yoshimura, Toshio; Takagi, Atsushi

    2004-09-01

    This paper presents the construction of a pneumatic active suspension system for a one-wheel car model using fuzzy reasoning and a disturbance observer. The one-wheel car model can be approximately described as a nonlinear two degrees of freedom system subject to excitation from a road profile. The active control is composed of fuzzy and disturbance controls, and functions by actuating a pneumatic actuator. A phase lead-lag compensator is inserted to counter the performance degradation due to the delay of the pneumatic actuator. The experimental result indicates that the proposed active suspension improves much the vibration suppression of the car model.

  7. Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

    PubMed

    Pan, Indranil; Das, Saptarshi; Gupta, Amitava

    2011-01-01

    An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers.

  8. Fuzzy Logic Controller Based on Observed Signals and a Genetic Algorithm Application with STATCOM for Power System Stabilization

    NASA Astrophysics Data System (ADS)

    Hongesombut, Komsan; Mitani, Yasunori; Tsuji, Kiichiro

    Fuzzy logic control has been applied to various applications in power systems. Its control rules and membership functions are typically obtained by trial and error methods or experience knowledge. Proposed here is the application of a micro-genetic algorithm (micro-GA) to simultaneously design optimal membership functions and control rules for STATCOM. First, we propose a simple approach to extract membership functions and fuzzy logic control rules based on observed signals. Then a proposed GA will be applied to optimize membership functions and its control rules. To validate the effectiveness of the proposed approach, several simulation studies have been performed on a multimachine power system. Simulation results show that the proposed fuzzy logic controller with STATCOM can effectively and robustly enhance the damping of oscillations.

  9. Fuzzy logic path planning system for collision avoidance by an autonomous rover vehicle

    NASA Technical Reports Server (NTRS)

    Murphy, Michael G.

    1993-01-01

    The Space Exploration Initiative of the United States will make great demands upon NASA and its limited resources. One aspect of great importance will be providing for autonomous (unmanned) operation of vehicles and/or subsystems in space flight and surface exploration. An additional, complicating factor is that much of the need for autonomy of operation will take place under conditions of great uncertainty or ambiguity. Issues in developing an autonomous collision avoidance subsystem within a path planning system for application in a remote, hostile environment that does not lend itself well to remote manipulation by Earth-based telecommunications is addressed. A good focus is unmanned surface exploration of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. Four major issues addressed are (1) avoidance of a fuzzy moving obstacle; (2) backoff from a deadend in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system. Examples of the need for collision avoidance by an autonomous rover vehicle on the surface of Mars with a moving obstacle would be wind-blown debris, surface flow or anomalies due to subsurface disturbances, another vehicle, etc. The other issues of backoff, sensor fusion, and adaptive learning are important in the overall path planning system.

  10. A fuzzy robust control scheme for vibration suppression of a nonlinear electromagnetic-actuated flexible system

    NASA Astrophysics Data System (ADS)

    Tavakolpour-Saleh, A. R.; Haddad, M. A.

    2017-03-01

    In this paper, a novel robust vibration control scheme, namely, one degree-of-freedom fuzzy active force control (1DOF-FAFC) is applied to a nonlinear electromagnetic-actuated flexible plate system. First, the flexible plate with clamped-free-clamped-free (CFCF) boundary conditions is modeled and simulated. Then, the validity of the simulation platform is evaluated through experiment. A nonlinear electromagnetic actuator is developed and experimentally modeled through a parametric system identification scheme. Next, the obtained nonlinear model of the actuator is applied to the simulation platform and performance of the proposed control technique in suppressing unwanted vibrations is investigated via simulation. A fuzzy controller is applied to the robust 1DOF control scheme to tune the controller gain using acceleration feedback. Consequently, an intelligent self-tuning vibration control strategy based on an inexpensive acceleration sensor is proposed in the paper. Furthermore, it is demonstrated that the proposed acceleration-based control technique owns the benefits of the conventional velocity feedback controllers. Finally, an experimental rig is developed to investigate the effectiveness of the 1DOF-FAFC scheme. It is found that the first, second, and third resonant modes of the flexible system are attenuated up to 74%, 81%, and 90% respectively through which the effectiveness of the proposed control scheme is affirmed.

  11. Fuzzy-Logic Subsumption Controller for Home Energy Management Systems

    SciTech Connect

    Ainsworth, Nathan; Johnson, Brian; Lundstrom, Blake

    2015-10-06

    Home Energy Management Systems (HEMS) are controllers that manage and coordinate the generation, storage, and loads in a home. These controllers are increasingly necessary to ensure that increasing penetrations of distributed energy resources are used effectively and do not disrupt the operation of the grid. In this paper, we propose a novel approach to HEMS design based on behavioral control methods, which do not require accurate models or predictions and are very responsive to changing conditions. We develop a proof-of-concept behavioral HEMS controller and show by simulation on an example home energy system that it capable of making context-dependent tradeoffs between goals under challenging conditions.

  12. Extending human perception of electromagnetic radiation to the UV region through biologically inspired photochromic fuzzy logic (BIPFUL) systems.

    PubMed

    Gentili, Pier Luigi; Rightler, Amanda L; Heron, B Mark; Gabbutt, Christopher D

    2016-01-25

    Photochromic fuzzy logic systems have been designed that extend human visual perception into the UV region. The systems are founded on a detailed knowledge of the activation wavelengths and quantum yields of a series of thermally reversible photochromic compounds. By appropriate matching of the photochromic behaviour unique colour signatures are generated in response differing UV activation frequencies.

  13. Fuzzy rule-based expert system for assessment severity of asthma.

    PubMed

    Zolnoori, Maryam; Zarandi, Mohammad Hossein Fazel; Moin, Mostafa; Teimorian, Shahram

    2012-06-01

    Prescription medicine for asthma at primary stages is based on asthma severity level. Despite major progress in discovering various variables affecting asthma severity levels, disregarding some of these variables by physicians, variables' inherent uncertainty, and assigning patients to limited categories of decision making are the major causes of underestimating asthma severity, and as a result low quality of life in asthmatic patients. In this paper, we provide a solution of intelligence fuzzy system for this problem. Inputs of this system are organized in five modules of respiratory symptoms, bronchial obstruction, asthma instability, quality of life, and asthma severity. Output of this system is degree of asthma severity in score (0-10). Evaluating performance of this system by 28 asthmatic patients reinforces that the system's results not only correspond with evaluations of physicians, but represent the slight differences of asthmatic patients placed in specific category introduced by guidelines.

  14. Fuzzy rule-based expert system for evaluating level of asthma control.

    PubMed

    Zolnoori, Maryam; Fazel Zarandi, Mohammad Hosain; Moin, Mostafa; Taherian, Mehran

    2012-10-01

    Asthma control is a final goal of asthma therapy process. Despite outstanding progress in discovering various variables affecting asthma control levels, disregarding some of them by physicians and variables' inherent uncertainty are the major causes of underestimating of asthma control levels and as a result asthma morbidity and mortality. In this paper, we provide an intelligent fuzzy system as a solution for this problem. Inputs of this system are composed of 14 variables organized in five modules of respiratory symptoms severity, bronchial obstruction, asthma instability, current treatment, and quality of life. Output of this system is degree of asthma control defined in the score (0-10). Evaluation of performance of this system by 42 asthmatic patients at asthma, allergy, immunology research center of Emam Khomeini hospital, Tehran, Iran reinforces that the system's results not only correspond with the evaluations of experienced asthma physicians, but represents slight differences in the levels of asthma control between asthmatic patients.

  15. Developing a fuzzy rule based cognitive map for total system safety assessment

    SciTech Connect

    Lemos, Francisco Luiz de; Sullivan, Terry

    2007-07-01

    Total System Performance Assessment, TSPA, for radioactive waste disposal is a multi and interdisciplinary task that is characterized by complex interactions between parameters and processes; lack of data; and ignorance regarding natural processes and conditions. The vagueness in the determination of ranges of values of parameters and identification of interacting processes pose further difficulties to the analysts with regard to the establishment of the relations between processes and parameters. More specifically the vagueness makes uncertainty propagation and sensitivity analysis challenging to analyze. To cope with these difficulties experts often use simplifications and linguistic terms to express their state of knowledge about a certain situation. For example, experts use terms such as 'low pH', 'very unlikely', etc to describe their perception about natural processes or conditions. In this work we propose the use of Fuzzy Cognitive Maps, FCM, for representation of interrelation between processes and parameters as well as to promote a better understanding of the system performance. Fuzzy cognitive maps are suited for the case where the causal relations are not clearly defined and, therefore, can not be represented by crisp values. In other words, instead of representing the quality of the interactions by crisp values, they are assigned degrees of truth. For example, we can assign values to the effect of one process on another such that (+) 1 corresponds to positive, (-) 1 to negative and 0 to neutral effects respectively. In this case the effect of a process A, on a process, B, can be depicted as function of the membership to the fuzzy set 'causal effect' of the cause process to the target one. One of the main advantages of this methodology would be that it allows one to aggregate the linguistic expressions as descriptions of processes. For example, a process can be known to have a 'very strong' positive effect on another one, or using fuzzy sets terminology

  16. An analytical fuzzy-based approach to ?-gain optimal control of input-affine nonlinear systems using Newton-type algorithm

    NASA Astrophysics Data System (ADS)

    Milic, Vladimir; Kasac, Josip; Novakovic, Branko

    2015-10-01

    This paper is concerned with ?-gain optimisation of input-affine nonlinear systems controlled by analytic fuzzy logic system. Unlike the conventional fuzzy-based strategies, the non-conventional analytic fuzzy control method does not require an explicit fuzzy rule base. As the first contribution of this paper, we prove, by using the Stone-Weierstrass theorem, that the proposed fuzzy system without rule base is universal approximator. The second contribution of this paper is an algorithm for solving a finite-horizon minimax problem for ?-gain optimisation. The proposed algorithm consists of recursive chain rule for first- and second-order derivatives, Newton's method, multi-step Adams method and automatic differentiation. Finally, the results of this paper are evaluated on a second-order nonlinear system.

  17. Fuzziness and Heterogeneity of Benthic Metacommunities in a Complex Transitional System

    PubMed Central

    Curiel, Daniele; Cossarini, Gianpiero; Melaku Canu, Donata; Rismondo, Andrea

    2012-01-01

    communities much better than any single property can. Our results also emphasize the importance of considering heterogeneity and fuzziness when working in natural systems. PMID:23285023

  18. Using a fuzzy expert system to generate a holistic quantitative index of groundwater sustainability

    NASA Astrophysics Data System (ADS)

    Fleming, S. W.; Wong, C.; Graham, G.

    2011-12-01

    Indicators and indices can be an effective method for tracking environmental conditions over time, and thus for assessing the effectiveness of policy measures or remediation activities. Relative to surface water resources, however, groundwater has received little attention in this regard. This is problematic: about 30% and 44% of the Canadian and American populations depend on groundwater resources, with localized reliance of up to 100%. Aquifers can also serve key functions in watershed hydrology by attenuating peak flows, providing baseflow and associated aquatic habitat, moderating water temperature, and providing transport pathways for contaminants from the land surface to the open freshwater environment. Here, we introduce a prototype groundwater sustainability index. It is holistic in the sense that it incorporates both quantity and quality indicators. The former is based on the signal-to-noise ratio of long-term water level trends as estimated via robust (rank-based) regression, whereas the latter is based on concentration of the chief contaminant of concern. A fuzzy inference system is employed to integrate these unlike metrics, and has the additional advantages of explicitly encoding expert knowledge and directly acknowledging subjectivity in environmental condition "grading" through the use of linguistic rules and fuzzy sets, respectively. The rule base is constructed such that poor environmental conditions captured by one measure would not be hidden by good environmental performance in another. A standard Mamdani (max-min) inference engine is used in conjunction with centroid defuzzification. The outcome is a fuzzy logic-based groundwater sustainability index (FGWSI) ranging from 0 to 100. The index is demonstrated using both synthetic and observational datasets, including examples from the Abbotsford-Sumas aquifer, an important and managerially challenging transboundary (Canada-US) water resource.

  19. Characterizing fluvial systems at basin scale by fuzzy signatures of hydromorphological drivers in data scarce environments

    NASA Astrophysics Data System (ADS)

    Schmitt, R.; Bizzi, S.; Castelletti, A.

    2014-06-01

    Despite the relevance of river hydromorphology (HYMO) for integrated water resource management, consistent geomorphic information at the scale of whole river basin is still scarce, especially in emerging economies. In this paper, we propose a new, scalable and globally applicable framework to analyze and classify fluvial systems in data-scarce environments. The framework is based on a data-driven analysis of a multivariate data set of 6 key hydro-morphologic drivers derived using freely available remote-sensing information and several in situ hydrological time series. Core of the framework is a fuzzy classifier that assigns a characteristic signature of HYMO drivers to individual river reaches. We demonstrate the framework on the Red River Basin, a large, trans-boundary river basin in Vietnam and China, where human-induced morphological change, concretely endangering local livelihoods, is contrasted by very limited HYMO information. The derived HYMO information covers spatial scales from the entire basin to individual reaches. It conveys relevant information on subbasin hydro-morphologic characteristic as well as on local geomorphologic forms and processes. The fuzzy classifier successfully distinguishes abrupt from continuous downstream change and spatially dissects the river system in segments with homogeneous hydro-morphologic forcings. Successful numerical modelling of morphologic forms and process rates based on the HYMO signatures indicates that the multivariate, basin-scale classification captures relevant morphological drivers, outperforms an analysis based on local drivers only, and can support river management from diverse, morphology related perspectives over a wide range of scales.

  20. Design and Implementation of an Intelligent Windowsill System Using Smart Handheld Device and Fuzzy Microcontroller.

    PubMed

    Wang, Jing-Min; Yang, Ming-Ta; Chen, Po-Lin

    2017-04-11

    With the advance of science and technology, people have a desire for convenient and comfortable living. Creating comfortable and healthy indoor environments is a major consideration for designing smart homes. As handheld devices become increasingly powerful and ubiquitous, this paper proposes an innovative use of smart handheld devices (SHD), using MIT App Inventor and fuzzy control, to perform the real-time monitoring and smart control of the designed intelligent windowsill system (IWS) in a smart home. A compact weather station that consists of environment sensors was constructed in the IWS for measuring of indoor illuminance, temperature-humidity, carbon dioxide (CO₂) concentration and outdoor rain and wind direction. According to the measured environment information, the proposed system can automatically send a command to a fuzzy microcontroller performed by Arduino UNO to fully or partly open the electric curtain and electric window for adapting to climate changes in the indoor and outdoor environment. Moreover, the IWS can automatically close windows for rain splashing on the window. The presented novel control method for the windowsill not only expands the SHD applications, but greatly enhances convenience to users. To validate the feasibility and effectiveness of the IWS, a laboratory prototype was built and confirmed experimentally.

  1. Medical diagnosis imaging systems: image and signal processing applications aided by fuzzy logic

    NASA Astrophysics Data System (ADS)

    Hata, Yutaka

    2010-04-01

    First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.

  2. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    PubMed

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance.

  3. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    PubMed

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

  4. Fuzzy logic controller versus classical logic controller for residential hybrid solar-wind-storage energy system

    NASA Astrophysics Data System (ADS)

    Derrouazin, A.; Aillerie, M.; Mekkakia-Maaza, N.; Charles, J. P.

    2016-07-01

    Several researches for management of diverse hybrid energy systems and many techniques have been proposed for robustness, savings and environmental purpose. In this work we aim to make a comparative study between two supervision and control techniques: fuzzy and classic logics to manage the hybrid energy system applied for typical housing fed by solar and wind power, with rack of batteries for storage. The system is assisted by the electric grid during energy drop moments. A hydrogen production device is integrated into the system to retrieve surplus energy production from renewable sources for the household purposes, intending the maximum exploitation of these sources over years. The models have been achieved and generated signals for electronic switches command of proposed both techniques are presented and discussed in this paper.

  5. The application of PID parameter self-tuning fuzzy controller in the constant-power speed control system of heading machine

    NASA Astrophysics Data System (ADS)

    Mao, Jun; Hou, Jian; Shen, Dong

    2013-03-01

    This article describes the control system of PID parameter self-tuning fuzzy controller. For cutting the coal of different hardness, adopt fuzzy techniques, automatically adjust the feed speed of operating mechanism, and maintain the control of operating mechanism of heading machine with constant power.

  6. 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.

  7. Analysis and Design of Robust H∞ Fault Estimation Observer With Finite-Frequency Specifications for Discrete-Time Fuzzy Systems.

    PubMed

    Zhang, Ke; Jiang, Bin; Shi, Peng; Xu, Jinfa

    2015-07-01

    This paper addresses the problem of fault estimation observer design with finite-frequency specifications for discrete-time Takagi-Sugeno (T-S) fuzzy systems. First, for such T-S fuzzy models, an H∞ fault estimation observer with pole-placement constraint is proposed to achieve fault estimation. Based on the generalized Kalman-Yakubovich-Popov lemma, the given finite-frequency observer possesses less conservatism compared with the design of the entire-frequency domain. Furthermore, the performance of the presented fault estimation observer is further enhanced by adding the degree of freedom. Finally, two examples are presented to illustrate the effectiveness of the proposed strategy.

  8. Greenhouse irrigation control system design based on ZigBee and fuzzy PID technology

    NASA Astrophysics Data System (ADS)

    Zhou, Bing; Yang, Qiliang; Liu, Kenan; Li, Peiqing; Zhang, Jing; Wang, Qijian

    In order to achieve the water demand information accurately detect of the greenhouse crop and its precision irrigation automatic control, this article has designed a set of the irrigated control system based on ZigBee and fuzzy PID technology, which composed by the soil water potential sensor, CC2530F256 wireless microprocessor, IAR Embedded Workbench software development platform. And the time of Irrigation as the output .while the amount of soil water potential and crop growth cycle as the input. The article depended on Greenhouse-grown Jatropha to verify the object, the results show that the system can irrigate timely and appropriately according to the soil water potential and water demend of the different stages of Jatropha growth , which basically meet the design requirements. Therefore, the system has broad application prospects in the amount of greenhouse crop of fine control irrigation.

  9. Decentralized adaptive fuzzy output feedback control of nonlinear interconnected systems with time-varying delay

    NASA Astrophysics Data System (ADS)

    Wang, Qin; Chen, Zuwen; Song, Aiguo

    2017-01-01

    A robust adaptive output-feedback control scheme based on K-filters is proposed for a class of nonlinear interconnected time-varying delay systems with immeasurable states. It is difficult to design the controller due to the existence of the immeasurable states and the time-delay couplings among interconnected subsystems. This difficulty is overcome by use of the fuzzy system, the K-filters and the appropriate Lyapunov-Krasovskii functional. Based on Lyapunov theory, the closed-loop control system is proved to be semi-global uniformly ultimately bounded (SGUUB), and the output tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the approach.

  10. Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference System

    PubMed Central

    Ahn, DaeHan; Park, Homin; Hwang, Seokhyun; Park, Taejoon

    2017-01-01

    Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrance by leveraging built-in smartphone sensors only. The results of our comprehensive evaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8% accuracy regardless of smartphone positions and vehicle types. PMID:28208795

  11. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    NASA Astrophysics Data System (ADS)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  12. Processing of microCT implant-bone systems images using Fuzzy Mathematical Morphology

    NASA Astrophysics Data System (ADS)

    Bouchet, A.; Colabella, L.; Omar, S.; Ballarre, J.; Pastore, J.

    2016-04-01

    The relationship between a metallic implant and the existing bone in a surgical permanent prosthesis is of great importance since the fixation and osseointegration of the system leads to the failure or success of the surgery. Micro Computed Tomography is a technique that helps to visualize the structure of the bone. In this study, the microCT is used to analyze implant-bone systems images. However, one of the problems presented in the reconstruction of these images is the effect of the iron based implants, with a halo or fluorescence scattering distorting the micro CT image and leading to bad 3D reconstructions. In this work we introduce an automatic method for eliminate the effect of AISI 316L iron materials in the implant-bone system based on the application of Compensatory Fuzzy Mathematical Morphology for future investigate about the structural and mechanical properties of bone and cancellous materials.

  13. A fuzzy knowledge-based decision support system for groundwater pollution risk evaluation.

    PubMed

    Uricchio, Vito F; Giordano, Raffaele; Lopez, Nicola

    2004-11-01

    In this paper we propose a decision support system that can provide information on the environmental impact of anthropic activities by examining their effects on groundwater quality. We use the combined value of both intrinsic vulnerability of a specific local aquifer, obtained by implementing a parametric managerial model (SINTACS), and a degree of hazard value, which takes into account specific human activities. Incomplete information is notoriously common in environmental planning. To overcome this deficiency we apply an algorithmic and a qualitative approach, based on expert judgment incorporated into the system's knowledge base. The decision support system takes into account the uncertainty of the environmental domain by using fuzzy logic and evaluates the reliability of the results according to information availability.

  14. Hybrid intelligent control scheme for air heating system using fuzzy logic and genetic algorithm

    SciTech Connect

    Thyagarajan, T.; Shanmugam, J.; Ponnavaikko, M.; Panda, R.C.

    2000-01-01

    Fuzzy logic provides a means for converting a linguistic control strategy, based on expert knowledge, into an automatic control strategy. Its performance depends on membership function and rule sets. In the traditional Fuzzy Logic Control (FLC) approach, the optimal membership is formed by trial-and-error method. In this paper, Genetic Algorithm (GA) is applied to generate the optimal membership function of FLC. The membership function thus obtained is utilized in the design of the Hybrid Intelligent Control (HIC) scheme. The investigation is carried out for an Air Heat System (AHS), an important component of drying process. The knowledge of the optimum PID controller designed, is used to develop the traditional FLC scheme. The computational difficulties in finding optimal membership function of traditional FLC is alleviated using GA In the design of HIC scheme. The qualitative performance indices are evaluated for the three control strategies, namely, PID, FLC and HIC. The comparison reveals that the HIC scheme designed based on the hybridization of FLC with GA performs better. Moreover, GA is found to be an effective tool for designing the FLC, eliminating the human interface required to generate the membership functions.

  15. Nonlinear airpath control of modern diesel powertrains: a fuzzy systems approach

    NASA Astrophysics Data System (ADS)

    Plianos, A.; Stobart, R. K.

    2011-02-01

    In this article, an adaptive dynamic feedback linearisation (DFL) control design for the air-path system of diesel engines with uncertain parameters and external driver commands is proposed. First, the linearising control law is derived for the nominal diesel plant. It achieves tracking of suitable references (corresponding to low emissions and fuel consumption) for both the air-fuel ratio and the fraction of the recirculated exhaust gas. The engine model used for control design is formulated as a Takagi-Sugeno fuzzy model, and a fuzzy estimation algorithm is used to identify the plant parameters. Then, the identified parameters are used to adapt the controller online. The simulated diesel engine is a medium duty Caterpillar 3126B with six cylinders, equipped with a variable geometry turbocharger and an exhaust gas recirculation valve. The proposed controller design is based on the reduced third-order mean value model and implemented as a closed-form DFL control law on the full-order model. The resulting controllers, with and without adaptation, are assessed through simulations with a software-in-the-loop architecture using dSpace simulator. The adaptive controller, in particular, exhibits good control performance, ensuring global stability and tracking of output references with zero steady state offset.

  16. Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes.

    PubMed

    Ling, Steve S H; Nguyen, Hung T

    2011-03-01

    Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.

  17. Fuzzy logic based sensor performance evaluation of vehicle mounted metal detector systems

    NASA Astrophysics Data System (ADS)

    Abeynayake, Canicious; Tran, Minh D.

    2015-05-01

    Vehicle Mounted Metal Detector (VMMD) systems are widely used for detection of threat objects in humanitarian demining and military route clearance scenarios. Due to the diverse nature of such operational conditions, operational use of VMMD without a proper understanding of its capability boundaries may lead to heavy causalities. Multi-criteria fitness evaluations are crucial for determining capability boundaries of any sensor-based demining equipment. Evaluation of sensor based military equipment is a multi-disciplinary topic combining the efforts of researchers, operators, managers and commanders having different professional backgrounds and knowledge profiles. Information acquired through field tests usually involves uncertainty, vagueness and imprecision due to variations in test and evaluation conditions during a single test or series of tests. This report presents a fuzzy logic based methodology for experimental data analysis and performance evaluation of VMMD. This data evaluation methodology has been developed to evaluate sensor performance by consolidating expert knowledge with experimental data. A case study is presented by implementing the proposed data analysis framework in a VMMD evaluation scenario. The results of this analysis confirm accuracy, practicability and reliability of the fuzzy logic based sensor performance evaluation framework.

  18. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    ERIC Educational Resources Information Center

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  19. A Classification Model and an Open E-Learning System Based on Intuitionistic Fuzzy Sets for Instructional Design Concepts

    ERIC Educational Resources Information Center

    Güyer, Tolga; Aydogdu, Seyhmus

    2016-01-01

    This study suggests a classification model and an e-learning system based on this model for all instructional theories, approaches, models, strategies, methods, and technics being used in the process of instructional design that constitutes a direct or indirect resource for educational technology based on the theory of intuitionistic fuzzy sets…

  20. Fuzzy modeling and simulation

    NASA Astrophysics Data System (ADS)

    Pedrycz, Witold

    1993-12-01

    The paradigm of fuzzy modelling entails development of relationships (dependencies) between the linguistic entities defined for system's variables. The key feature of the fuzzy models pertains to their significant flexibility so they could easily be modified to comply with the principle of incompatibility. Considering the existing panoply of fuzzy models one can easily conclude that most of them are embraced under an umbrella of a single conceptual structure. From a functional point of view this structure is perceived as a combination of the two conceptual interfaces and a single processing block aimed at developing calculus of the linguistic labels. The interfaces produce all the links that are necessary to combine the physical (numerical) level of the real-world system with that of a conceptual character realized within the fuzzy model and articulated at the level of the linguistic entities. The presentation will address the main methodological aspects concerning these functional components with a particular emphasis placed on the associated design principles. The main issues dominating the design of the interfaces pertain to the implemented level of information granularity, optimality of linguistic labels, and linguistic-to-numerical transformations. The processing level of the fuzzy modelling will be considered through the use of fuzzy neural networks. These distributed computing structures are highly heterogeneous as they are constructed with the aid of several distinct types of logic-oriented neurons. The advantages of the fuzzy neural networks such as an implicit scheme of knowledge encapsulation that is carried out there will be discussed in detail.

  1. Selecting Sustainability Indicators for Small to Medium Sized Urban Water Systems Using Fuzzy-ELECTRE.

    PubMed

    Chhipi-Shrestha, Gyan; Hewage, Kasun; Sadiq, Rehan

    2017-03-01

      Urban water systems (UWSs) are challenged by the sustainability perspective. Certain limitations of the sustainability of centralized UWSs and decentralized household level wastewater treatments can be overcome by managing UWSs at an intermediate scale, referred to as small to medium sized UWSs (SMUWSs). SMUWSs are different from large UWSs, mainly in terms of smaller infrastructure, data limitation, smaller service area, and institutional limitations. Moreover, sustainability assessment systems to evaluate the sustainability of an entire UWS are very limited and confined only to large UWSs. This research addressed the gap and has developed a set of 38 applied sustainability performance indicators (SPIs) by using fuzzy-Elimination and Choice Translating Reality (ELECTRE) I outranking method to assess the sustainability of SMUWSs. The developed set of SPIs can be applied to existing and new SMUWSs and also provides a flexibility to include additional SPIs in the future based on the same selection criteria.

  2. Fuzzy Cognitive Map scenario-based medical decision support systems for education.

    PubMed

    Georgopoulos, Voula C; Chouliara, Spyridoula; Stylios, Chrysostomos D

    2014-01-01

    Soft Computing (SC) techniques are based on exploiting human knowledge and experience and they are extremely useful to model any complex decision making procedure. Thus, they have a key role in the development of Medical Decision Support Systems (MDSS). The soft computing methodology of Fuzzy Cognitive Maps has successfully been used to represent human reasoning and to infer conclusions and decisions in a human-like way and thus, FCM-MDSSs have been developed. Such systems are able to assist in critical decision-making, support diagnosis procedures and consult medical professionals. Here a new methodology is introduced to expand the utilization of FCM-MDSS for learning and educational purposes using a scenario-based learning (SBL) approach. This is particularly important in medical education since it allows future medical professionals to safely explore extensive "what-if" scenarios in case studies and prepare for dealing with critical adverse events.

  3. A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

    PubMed Central

    Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José

    2009-01-01

    In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160

  4. FPGA implementation of neuro-fuzzy system with improved PSO learning.

    PubMed

    Karakuzu, Cihan; Karakaya, Fuat; Çavuşlu, Mehmet Ali

    2016-07-01

    This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources.

  5. A neuro-fuzzy system for extracting environment features based on ultrasonic sensors.

    PubMed

    Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José

    2009-01-01

    In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case.

  6. 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.

  7. Adaptive neuro-fuzzy inference system to improve the power quality of a split shaft microturbine power generation system

    NASA Astrophysics Data System (ADS)

    Oğuz, Yüksel; Üstün, Seydi Vakkas; Yabanova, İsmail; Yumurtaci, Mehmet; Güney, İrfan

    2012-01-01

    This article presents design of adaptive neuro-fuzzy inference system (ANFIS) for the turbine speed control for purpose of improving the power quality of the power production system of a split shaft microturbine. To improve the operation performance of the microturbine power generation system (MTPGS) and to obtain the electrical output magnitudes in desired quality and value (terminal voltage, operation frequency, power drawn by consumer and production power), a controller depended on adaptive neuro-fuzzy inference system was designed. The MTPGS consists of the microturbine speed controller, a split shaft microturbine, cylindrical pole synchronous generator, excitation circuit and voltage regulator. Modeling of dynamic behavior of synchronous generator driver with a turbine and split shaft turbine was realized by using the Matlab/Simulink and SimPowerSystems in it. It is observed from the simulation results that with the microturbine speed control made with ANFIS, when the MTPGS is operated under various loading situations, the terminal voltage and frequency values of the system can be settled in desired operation values in a very short time without significant oscillation and electrical production power in desired quality can be obtained.

  8. Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

    NASA Astrophysics Data System (ADS)

    Yoshimura, Toshio

    2016-02-01

    This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.

  9. Assessment of a fuzzy based flood forecasting system optimized by simulated annealing

    NASA Astrophysics Data System (ADS)

    Reyhani Masouleh, Aida; Pakosch, Sabine; Disse, Markus

    2010-05-01

    Flood forecasting is an important tool to mitigate harmful effects of floods. Among the many different approaches for forecasting, Fuzzy Logic (FL) is one that has been increasingly applied over the last decade. This method is principally based on the linguistic description of Rule Systems (RS). A RS is a specific combination of membership functions of input and output variables. Setting up the RS can be implemented either automatically or manually, the choice of which can strongly influence the resulting rule systems. It is therefore the objective of this study to assess the influence that the parameters of an automated rule generation based on Simulated Annealing (SA) have on the resulting RS. The study area is the upper Main River area, located in the northern part of Bavaria, Germany. The data of Mainleus gauge with area of 1165 km2 was investigated in the whole period of 1984 and 2004. The highest observed discharge of 357 m3/s was recorded in 1995. The input arguments of the FL model were daily precipitation, forecasted precipitation, antecedent precipitation index, temperature and melting rate. The FL model of this study has one output variable, daily discharge and was independently set up for three different forecast lead times, namely one-, two- and three-days ahead. In total, each RS comprised 55 rules and all input and output variables were represented by five sets of trapezoidal and triangular fuzzy numbers. Simulated Annealing, which is a converging optimum solution algorithm, was applied for optimizing the RSs in this study. In order to assess the influence of its parameters (number of iterations, temperature decrease rate, initial value for generating random numbers, initial temperature and two other parameters), they were individually varied while keeping the others fixed. With each of the resulting parameter sets, a full-automatic SA was applied to gain optimized fuzzy rule systems for flood forecasting. Evaluation of the performance of the

  10. Completeness and regularity of generalized fuzzy graphs.

    PubMed

    Samanta, Sovan; Sarkar, Biswajit; Shin, Dongmin; Pal, Madhumangal

    2016-01-01

    Fuzzy graphs are the backbone of many real systems like networks, image, scheduling, etc. But, due to some restriction on edges, fuzzy graphs are limited to represent for some systems. Generalized fuzzy graphs are appropriate to avoid such restrictions. In this study generalized fuzzy graphs are introduced. In this study, matrix representation of generalized fuzzy graphs is described. Completeness and regularity are two important parameters of graph theory. Here, regular and complete generalized fuzzy graphs are introduced. Some properties of them are discussed. After that, effective regular graphs are exemplified.

  11. Network-Based Output Tracking Control for a Class of T-S Fuzzy Systems That Can Not Be Stabilized by Nondelayed Output Feedback Controllers.

    PubMed

    Zhang, Dawei; Han, Qing-Long; Jia, Xinchun

    2015-08-01

    This paper investigates network-based output tracking control for a T-S fuzzy system that can not be stabilized by a nondelayed fuzzy static output feedback controller, but can be stabilized by a delayed fuzzy static output feedback controller. By intentionally introducing a communication network that produces proper network-induced delays in the feedback control loop, a stable and satisfactory tracking control can be ensured for the T-S fuzzy system. Due to the presence of network-induced delays, the fuzzy system and the fuzzy tracking controller operate in an asynchronous way. Taking the asynchronous operation and network-induced delays into consideration, the network-based tracking control system is modeled as an asynchronous T-S fuzzy system with an interval time-varying delay. A new delay-dependent criterion for L2 -gain tracking performance is derived by using the deviation bounds of asynchronous normalized membership functions and a complete Lyapunov-Krasovskii functional. Applying a particle swarm optimization technique with the feasibility of the derived criterion, a novel design algorithm is presented to determine the minimum L2 -gain tracking performance and control gains simultaneously. The effectiveness of the proposed method is illustrated by performing network-based output tracking control of a Duffing-Van der Pol's oscillator.

  12. The International System of Units (SI).

    ERIC Educational Resources Information Center

    Page, Chester H., Ed.; Vigoureux, Paul, Ed.

    This document gives definitions and symbols for the basic units of measure, for derived units, and for supplementary units. Decimal multiples and sub-multiples of units and units outside the International System also are discussed. Appendix I reproduces the decisions made on units and on the International System by two committees (the General…

  13. Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a sugar plant

    NASA Astrophysics Data System (ADS)

    Aggarwal, Anil Kr.; Kumar, Sanjeev; Singh, Vikram

    2016-08-01

    The binary states, i.e., success or failed state assumptions used in conventional reliability are inappropriate for reliability analysis of complex industrial systems due to lack of sufficient probabilistic information. For large complex systems, the uncertainty of each individual parameter enhances the uncertainty of the system reliability. In this paper, the concept of fuzzy reliability has been used for reliability analysis of the system, and the effect of coverage factor, failure and repair rates of subsystems on fuzzy availability for fault-tolerant crystallization system of sugar plant is analyzed. Mathematical modeling of the system is carried out using the mnemonic rule to derive Chapman-Kolmogorov differential equations. These governing differential equations are solved with Runge-Kutta fourth-order method.

  14. A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference.

    PubMed

    Chiang, Tzu-Chiang; Liang, Wen-Hua

    2015-09-01

    In the present society, most families are double-income families, and as the long-term care is seriously short of manpower, it contributes to the rapid development of tele-homecare equipment, and the smart home care system gradually emerges, which assists the elderly or patients with chronic diseases in daily life. This study aims at interaction between persons under care and the system in various living spaces, as based on motion-sensing interaction, and the context-aware smart home care system is proposed. The system stores the required contexts in knowledge ontology, including the physiological information and environmental information of the person under care, as the database of decision. The motion-sensing device enables the person under care to interact with the system through gestures. By the inference mechanism of fuzzy theory, the system can offer advice and rapidly execute service, thus, implementing the EHA. In addition, the system is integrated with the functions of smart phone, tablet PC, and PC, in order that users can implement remote operation and share information regarding the person under care. The health care system constructed in this study enables the decision making system to probe into the health risk of each person under care; then, from the view of preventive medicine, and through a composing system and simulation experimentation, tracks the physiological trend of the person under care, and provides early warning service, thus, promoting smart home care.

  15. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    NASA Astrophysics Data System (ADS)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  16. Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

    NASA Technical Reports Server (NTRS)

    Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

    1991-01-01

    Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

  17. Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays.

    PubMed

    Zheng, Ying; Fang, Huajing; Wang, Hua O

    2006-08-01

    A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches.

  18. Prediction of photonic crystal fiber characteristics by Neuro-Fuzzy system

    NASA Astrophysics Data System (ADS)

    Pourmahyabadi, M.; Mohammad Nejad, S.

    2009-10-01

    The most common methods applied in the analysis of photonic crystal fibers (PCFs) are finite difference time/frequency domain (FDTD/FDFD) method and finite element method (FEM). These methods are very general and reliable (well tested). They describe arbitrary structure but are numerically intensive and require detailed treatment of boundaries and complex definition of calculation mesh. So these conventional models that simulate the photonic response of PCFs are computationally expensive and time consuming. Therefore, a practical design process with trial and error cannot be done in a reasonable amount of time. In this article, an artificial intelligence method such as Neuro-Fuzzy system is used to establish a model that can predict the properties of PCFs. Simulation results show that this model is remarkably effective in predicting the properties of PCF such as dispersion, dispersion slope and loss over the C communication band.

  19. [Research on the Application of Fuzzy Logic to Systems Analysis and Control

    NASA Technical Reports Server (NTRS)

    1998-01-01

    Research conducted with the support of NASA Grant NCC2-275 has been focused in the main on the development of fuzzy logic and soft computing methodologies and their applications to systems analysis and control. with emphasis 011 problem areas which are of relevance to NASA's missions. One of the principal results of our research has been the development of a new methodology called Computing with Words (CW). Basically, in CW words drawn from a natural language are employed in place of numbers for computing and reasoning. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW.

  20. A study of fuzzy logic ensemble system performance on face recognition problem

    NASA Astrophysics Data System (ADS)

    Polyakova, A.; Lipinskiy, L.

    2017-02-01

    Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

  1. PID-Type Fuzzy Control for Anti-Lock Brake Systems with Parameter Adaptation

    NASA Astrophysics Data System (ADS)

    Chen, Chih-Keng; Shih, Ming-Chang

    In this research, a platform is built to accomplish a series of experiments to control the Antilock Brake System (ABS). A commercial ABS module controlled by a controller is installed and tested on the platform. The vehicle and tire models are deduced and simulated by a personal computer for real time control. An adaptive PID-type fuzzy control scheme is used. Two on-off conversion methods: pulse width modulation (PWM) and conditional on-off, are used to control the solenoid valves in the ABS module. With the pressure signal feedbacks in the caliper, vehicle dynamics and wheel speeds are computed during braking. Road surface conditions, vehicle weight and control schemes are varied in the experiments to study braking properties.

  2. Fault Analysis of Analog Electronic Systems: Algorithms Based on Fuzzy Sets

    DTIC Science & Technology

    1979-06-01

    Listing of the Input to the NOPAL 98 Fig. 4.1 Fault Isolation using a Fuzzy Measure 113 * Fig. 4.2 Simplified Diagram of Fault Isolation 123 Method...regions with the highest fuzzy memberships are added into the test data. A partial listing of the input to the NOPAL is given in Fig. 3.6.4. This program is

  3. A composite self tuning strategy for fuzzy control of dynamic systems

    NASA Technical Reports Server (NTRS)

    Shieh, C.-Y.; Nair, Satish S.

    1992-01-01

    The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

  4. Identification of piecewise affine systems based on fuzzy PCA-guided robust clustering technique

    NASA Astrophysics Data System (ADS)

    Khanmirza, Esmaeel; Nazarahari, Milad; Mousavi, Alireza

    2016-12-01

    Hybrid systems are a class of dynamical systems whose behaviors are based on the interaction between discrete and continuous dynamical behaviors. Since a general method for the analysis of hybrid systems is not available, some researchers have focused on specific types of hybrid systems. Piecewise affine (PWA) systems are one of the subsets of hybrid systems. The identification of PWA systems includes the estimation of the parameters of affine subsystems and the coefficients of the hyperplanes defining the partition of the state-input domain. In this paper, we have proposed a PWA identification approach based on a modified clustering technique. By using a fuzzy PCA-guided robust k-means clustering algorithm along with neighborhood outlier detection, the two main drawbacks of the well-known clustering algorithms, i.e., the poor initialization and the presence of outliers, are eliminated. Furthermore, this modified clustering technique enables us to determine the number of subsystems without any prior knowledge about system. In addition, applying the structure of the state-input domain, that is, considering the time sequence of input-output pairs, provides a more efficient clustering algorithm, which is the other novelty of this work. Finally, the proposed algorithm has been evaluated by parameter identification of an IGV servo actuator. Simulation together with experiment analysis has proved the effectiveness of the proposed method.

  5. A fuzzy clustering based segmentation system as support to diagnosis in medical imaging.

    PubMed

    Masulli, F; Schenone, A

    1999-06-01

    In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularly suitable for handling a decision making process concerning segmentation of multimodal medical images. By using the possibilistic c-means algorithm as a refinement of a neural network based clustering algorithm named capture effect neural network, we developed the possibilistic neuro fuzzy c-means algorithm (PNFCM). In this paper the PNFCM has been applied to two different multimodal data sets and the results have been compared to those obtained by using the classical fuzzy c-means algorithm. Furthermore, a discussion is presented about the role of fuzzy clustering as a support to diagnosis in medical imaging.

  6. A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

    SciTech Connect

    Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken

    2010-12-15

    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, 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)

  7. A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

    NASA Astrophysics Data System (ADS)

    Gholami, V.; Khaleghi, M. R.; Sebghati, M.

    2016-12-01

    The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

  8. Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming

    NASA Astrophysics Data System (ADS)

    Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

    2016-04-01

    This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to

  9. Adaptive fuzzy control with smooth inverse for nonlinear systems preceded by non-symmetric dead-zone

    NASA Astrophysics Data System (ADS)

    Wang, Xingjian; Wang, Shaoping

    2016-07-01

    In this study, the adaptive output feedback control problem of a class of nonlinear systems preceded by non-symmetric dead-zone is considered. To cope with the possible control signal chattering phenomenon which is caused by non-smooth dead-zone inverse, a new smooth inverse is proposed for non-symmetric dead-zone compensation. For the systematic design procedure of the adaptive fuzzy control algorithm, we combine the backstepping technique and small-gain approach. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown system nonlinearities. The closed-loop stability is studied by using small gain theorem and the closed-loop system is proved to be semi-globally uniformly ultimately bounded. Simulation results indicate that, compared to the algorithm with the non-smooth inverse, the proposed control strategy can achieve better tracking performance and the chattering phenomenon can be avoided effectively.

  10. Improvement of the Performance of an Electrocoagulation Process System Using Fuzzy Control of pH.

    PubMed

    Demirci, Yavuz; Pekel, Lutfiye Canan; Altinten, Ayla; Alpbaz, Mustafa

    2015-12-01

    The removal efficiencies of electrocoagulation (EC) systems are highly dependent on the initial value of pH. If an EC system has an acidic influent, the pH of the effluent increases during the treatment process; conversely, if such a system has an alkaline influent, the pH of the effluent decreases during the treatment process. Thus, changes in the pH of the wastewater affect the efficiency of the EC process. In this study, we investigated the dynamic effects of pH. To evaluate approaches for preventing increases in the pH of the system, the MATLAB/Simulink program was used to develop and evaluate an on-line computer-based system for pH control. The aim of this work was to study Proportional-Integral-Derivative (PID) control and fuzzy control of the pH of a real textile wastewater purification process using EC. The performances and dynamic behaviors of these two control systems were evaluated based on determinations of COD, colour, and turbidity removal efficiencies.

  11. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks.

    PubMed

    Hernández Díaz, Vicente; Martínez, José-Fernán; Lucas Martínez, Néstor; del Toro, Raúl M

    2015-09-18

    The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

  12. Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft

    NASA Astrophysics Data System (ADS)

    Sadok, Mokhtar; Zakrzewski, Radek; Zeliff, Bob

    2005-02-01

    Conventional smoke detection systems currently installed onboard aircraft are often subject to high rates of false alarms. Under current procedures, whenever an alarm is issued the pilot is obliged to release fire extinguishers and to divert to the nearest airport. Aircraft diversions are costly and dangerous in some situations. A reliable detection system that minimizes false-alarm rate and allows continuous monitoring of cargo compartments is highly desirable. A video-based system has been recently developed by Goodrich Corporation to address this problem. The Cargo Fire Verification System (CFVS) is a multi camera system designed to provide live stream video to the cockpit crew and to perform hotspot, fire, and smoke detection in aircraft cargo bays. In addition to video frames, the CFVS uses other sensor readings to discriminate between genuine events such as fire or smoke and nuisance alarms such as fog or dust. A Mamdani-type fuzzy inference engine is developed to provide approximate reasoning for decision making. In one implementation, Gaussian membership functions for frame intensity-based features, relative humidity, and temperature are constructed using experimental data to form the system inference engine. The CFVS performed better than conventional aircraft smoke detectors in all standardized tests.

  13. A fuzzy system for helping medical diagnosis of malformations of cortical development.

    PubMed

    Alayón, Silvia; Robertson, Richard; Warfield, Simon K; Ruiz-Alzola, Juan

    2007-06-01

    Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.

  14. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks

    PubMed Central

    Hernández Díaz, Vicente; Martínez, José-Fernán; Lucas Martínez, Néstor; del Toro, Raúl M.

    2015-01-01

    The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries. PMID:26393612

  15. A Fuzzy System for Helping Medical Diagnosis of Malformations of Cortical Development

    PubMed Central

    Alayón, Silvia; Robertson, Richard; Warfield, Simon K.; Ruiz-Alzola, Juan

    2007-01-01

    Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper. PMID:17197247

  16. Creating an Internal Content Management System

    ERIC Educational Resources Information Center

    Sennema, Greg

    2004-01-01

    In this article, the author talks about an internal content management system that they have created at Calvin College. It is a hybrid of CMS and intranet that organizes Web site content and a variety of internal tools to help librarians complete their daily tasks. Hobbes is a Web-based tool that uses Common Gateway Interface (CGI) scripts written…

  17. Fuzzy Deterrence

    DTIC Science & Technology

    2010-05-01

    cognitive map. Three examples illustrate fuzzy cognitive maps‘ potential for understanding a non -state actor‘s decision-making calculus and...of the Cold War, the United States has wrestled with how rational deterrence applies to non -state actors in today’s complex security environment...Fuzzy logic’s themes of flexibility, adaptability, and ambiguity lay the foundation for applying fuzzy logic to non -state actor deterrence. Because

  18. PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid Applications.

    PubMed

    Rohini, G; Jamuna, V

    2016-01-01

    This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results.

  19. PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid Applications

    PubMed Central

    Rohini, G.; Jamuna, V.

    2016-01-01

    This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results. PMID:27294189

  20. 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.

  1. The International Space Station: Systems and Science

    NASA Technical Reports Server (NTRS)

    Giblin, Timothy W.

    2010-01-01

    ISS Program Mission: Safely build, operate, and utilize a permanent human outpost in space through an international partnership of government, industry, and academia to advance exploration of the solar system, conduct scientific research, and enable commerce in space.

  2. Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems.

    PubMed

    Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

    2016-01-01

    The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance-performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

  3. Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems

    PubMed Central

    Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

    2016-01-01

    The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance–performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system. PMID:27598390

  4. Prediction analysis and comparison between agriculture and mining stocks in Indonesia by using adaptive neuro-fuzzy inference system (ANFIS)

    NASA Astrophysics Data System (ADS)

    Mahandrio, Irsantyo; Budi, Andriantama; Liong, The Houw; Purqon, Acep

    2015-09-01

    The growing patterns in cultural and mining sectors are interesting particularly in developed country such as in Indonesia. Here, we investigate the local characteristics of stocks between the sectors of agriculture and mining which si representing two leading companies and two common companies in these sectors. We analyze the prediction by using Adaptive Neuro Fuzzy Inference System (ANFIS). The type of Fuzzy Inference System (FIS) is Sugeno type with Generalized Bell membership function (Gbell). Our results show that ANFIS is a proper method to predicting the stock market with the RMSE : 0.14% for AALI and 0.093% for SGRO representing the agriculture sectors, meanwhile, 0.073% for ANTM and 0.1107% for MDCO representing the mining sectors.

  5. Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process.

    PubMed

    Huang, Mingzhi; Ma, Yongwen; Wan, Jinquan; Wang, Yan; Chen, Yangmei; Yoo, Changkyoo

    2014-10-01

    Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.

  6. Internal coaxial cable seal system

    DOEpatents

    Hall, David R.; Sneddon, Cameron; Dahlgren, Scott Steven; Briscoe, Michael A.

    2006-07-25

    The invention is a seal system for a coaxial cable and is placed within the coaxial cable and its constituent components. A series of seal stacks including load ring components and elastomeric rings are placed on load bearing members within the coaxial cable sealing the annular space between the coaxial cable and an electrical contact passing there through. The coaxial cable is disposed within drilling components to transmit electrical signals between drilling components within a drill string. The seal system can be used in a variety of downhole components, such as sections of pipe in a drill string, drill collars, heavy weight drill pipe, and jars.

  7. Negotiation Support Agent Based on Fuzzy Decision Making by Genetic Programming with the Coupled Chaos System

    NASA Astrophysics Data System (ADS)

    Matsumura, Koki; Goto, Michihiko; Hamamatsu, Yoshio

    This paper describes a negotiation agent system based on the fuzzy decision making. The method of seeking appropriate membership functions and a reasonable agreement point was examined by means of the genetic programming technique with the coupled chaos system, which is an intelligent principle. The negotiation rule is based on the negotiation model expressed by the utility theory in the process of decision making. And the concession process was modified with the opponent’s movement and the persistence of each negotiator. In order to search for a membership function more efficiently, the dynamic state of symbiosis between individuals, which was caused by the coupled chaos system, was taken advantage of. Then the effectiveness of the technique was examined by applying it to a practical negotiation case which needs cooperative decision making. As a result, the following findings were obtained. This technique helps discover practicable membership functions in a vast search area, and achieve the solution search with high efficiency. This technique is also considered to be applied to the negotiation support easily.

  8. A high performance, ad-hoc, fuzzy query processing system for relational databases

    NASA Technical Reports Server (NTRS)

    Mansfield, William H., Jr.; Fleischman, Robert M.

    1992-01-01

    Database queries involving imprecise or fuzzy predicates are currently an evolving area of academic and industrial research. Such queries place severe stress on the indexing and I/O subsystems of conventional database environments since they involve the search of large numbers of records. The Datacycle architecture and research prototype is a database environment that uses filtering technology to perform an efficient, exhaustive search of an entire database. It has recently been modified to include fuzzy predicates in its query processing. The approach obviates the need for complex index structures, provides unlimited query throughput, permits the use of ad-hoc fuzzy membership functions, and provides a deterministic response time largely independent of query complexity and load. This paper describes the Datacycle prototype implementation of fuzzy queries and some recent performance results.

  9. Application of a self-tuning fuzzy PI-PD controller in an active anti-roll bar system for a passenger car

    NASA Astrophysics Data System (ADS)

    Muniandy, V.; Samin, P. M.; Jamaluddin, H.

    2015-11-01

    A fuzzy proportional-integral-derivative (PID) controller has not been widely investigated for active anti-roll bar (AARB) application due to its unspecific mathematical analysis and the derivative kick problem. This paper briefly explains how the derivative kick problem arises due to the nature of the PID controller as well as the conventional fuzzy PID controller in association with an AARB. There are two types of controllers proposed in this paper: self-tuning fuzzy proportional-integral-proportional-derivative (STF PI-PD) and PI-PD-type fuzzy controller. Literature reveals that the PI-PD configuration can avoid the derivative kick, unlike the standard PID configuration used in fuzzy PID controllers. STF PI-PD is a new controller proposed and presented in this paper, while the PI-PD-type fuzzy controller was developed by other researchers for robotics and automation applications. Some modifications were made on these controllers in order to make them work with an AARB system. The performances of these controllers were evaluated through a series of handling tests using a full car model simulated in MATLAB Simulink. The simulation results were compared with the performance of a passive anti-roll bar and the conventional fuzzy PID controller in order to show improvements and practicality of the proposed controllers. Roll angle signal was used as input for all the controllers. It is found that the STF PI-PD controller is able to suppress the derivative kick problem but could not reduce the roll motion as much as the conventional fuzzy PID would. However, the PI-PD-type fuzzy controller outperforms the rest by improving ride and handling of a simulated passenger car significantly.

  10. Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.

    PubMed

    Mohammadzadeh, Ardashir; Ghaemi, Sehraneh

    2015-09-01

    This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown.

  11. Fuzzy logic

    NASA Technical Reports Server (NTRS)

    Zadeh, Lofti A.

    1988-01-01

    The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

  12. Assessment of Benefits and Drawbacks of Using Fuzzy Logic, Especially in Fire Control Systems

    DTIC Science & Technology

    1994-03-01

    classitied by :W. Pelt classitication date :September 20, 1993 jp:op:ved ci.’ :::: - - - - title :ongerubriceerd manage mentuittreksel :Ongerubriceerd...kanonsysteem het gebruik van "fuzzy reasoning" technieken voorkwam. Dit voorstel is gedaan door SIGNAAL, in een opdracht van de KM. Het TNO-FEL had een...van bet gebruik van fuzzy logic in vergelijking met ’conventionele’ methoden. Geconcludeerd wordt dat er in principe goede mogelijkhieden voor

  13. Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.

    PubMed

    Arthi, K; Tamilarasi, A

    2008-11-01

    The major challenge in medical field is to diagnose disorder rather than a disease. In this paper, a neuro fuzzy based model is designed for identification or diagnosis of autism. The problematic areas are gathered from every individual and the related linguistic inputs are converted into fuzzy input values which are in turn given as input to feed forward multilayer neural network. The network is trained using back propagation training algorithm and tested for its performance with the expertise.

  14. A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems

    NASA Astrophysics Data System (ADS)

    Zhong, Xu; Zhou, Yu

    2014-05-01

    This paper presents a decentralized multi-robot motion control strategy to facilitate a multi-robot system, comprised of collaborative mobile robots coordinated through wireless communications, to form and maintain desired wireless communication coverage in a realistic environment with unstable wireless signaling condition. A fuzzy neural network controller is proposed for each robot to maintain the wireless link quality with its neighbors. The controller is trained through reinforcement learning to establish the relationship between the wireless link quality and robot motion decision, via consecutive interactions between the controller and environment. The tuned fuzzy neural network controller is applied to a multi-robot deployment process to form and maintain desired wireless communication coverage. The effectiveness of the proposed control scheme is verified through simulations under different wireless signal propagation conditions.

  15. A new efficient hybrid intelligent method for nonlinear dynamical systems identification: The Wavelet Kernel Fuzzy Neural Network

    NASA Astrophysics Data System (ADS)

    Loussifi, Hichem; Nouri, Khaled; Benhadj Braiek, Naceur

    2016-03-01

    In this paper a hybrid computational intelligent approach of combining kernel methods with wavelet Multi-resolution Analysis (MRA) is presented for fuzzy wavelet network construction and initialization. Mother wavelets are used as activation functions for the neural network structure, and as kernel functions in the machine learning process. By choosing precise values of scale parameters based on the windowed scalogram representation of the Continuous Wavelet Transform (CWT), a set of kernel parameters is taken to construct the proposed Wavelet Kernel based Fuzzy Neural Network (WK-FNN) with an efficient initialization technique based on the use of wavelet kernels in Support Vector Machine for Regression (SVMR). Simulation examples are given to test usability and effectiveness of the proposed hybrid intelligent method in the system identification of dynamic plants and in the prediction of a chaotic time series. It is seen that the proposed WK-FNN achieves higher accuracy and has good performance as compared to other methods.

  16. Carbon account of forest ecosystems as a fuzzy system: a case study for Northern Eurasia

    NASA Astrophysics Data System (ADS)

    Shvidenko, A.; Shchepashchenko, D.; Kraxner, F.; Maksyutov, S. S.

    2015-12-01

    We consider practicality of a verified account of Net Ecosystem Carbon Budget for forest ecosystems (FCA) that supposes reliable assessment of uncertainties, i.e. understanding "uncertainty of uncertainties". The FCA is a fuzzy (underspecified) system, of which membership function is inherently stochastic. Thus, any individually used method of FCA is not able to estimate structural uncertainties and usually reported "within method" uncertainties are inevitably partial. Attempting at estimation of "full uncertainties" of the studied system we follow the requirements of applied systems analysis integrating the major methods of terrestrial ecosystems carbon account, assessing the uncertainties "within method" for intermediate and final indicators of FCA with their following mutual constrains. Landscape-ecosystem approach (LEA) 1) serves for strict systems designing the account, 2) contains all relevant spatially distributed empirical and semi-empirical data and models, and 3) is presented in form of an Integrated Land Information System (ILIS). By-pixel parametrization of forest cover is provided by utilizing multi-sensor remote sensing data (12 RS products used) within GEO-wiki platform and other relevant information based on special optimization algorithms. Major carbon fluxes within the LEA (NPP, HR, disturbances etc.) are estimated based on fusion of empirical data with process-based elements by sets of regionally distributed models. Uncertainties within LEA are assessed for each module and at each step of the account. "Within method" results and uncertainties (including LEA, process-based models, eddy covariance, and inverse modelling) are harmonized based on the Bayesian approach. The above methodology have been applied to carbon account of Russian forests for 2000-2010; uncertainties of the FCA for individual years were estimated in limits of 25%. We discussed strengths and weaknesses of the approach, system requirements to different methods of FCA, information

  17. Introducing a new learning method for fuzzy descriptor systems with the aid of spectral analysis to forecast solar activity

    NASA Astrophysics Data System (ADS)

    Mirmomeni, Masoud; Shafiee, Masoud; Lucas, Caro; Araabi, Babak Nadjar

    2006-12-01

    In the last two decades, researches indicate that the physical precursor and solar dynamo techniques are preferred as practical tools for long term prediction of solar activity. But, why more than 23 cycles of solar activity history should be omitted and just use the empirical methods or simple autoregressive methods on the basis of observations for the latest eight cycles? In this article, a method based on fuzzy descriptor systems (as a generalization of ordinary Takagi Sugeno (T S) neuro-fuzzy models), developed by the authors to yield a satisfactory solution to the unresolved problem of nonlinear descriptor system identification, and singular spectrum analysis (SSA) as one of the spectral analysis is proposed to forecast some of solar activity's indexes in the way that, a fuzzy descriptor model is optimized for each of the principal components obtained from SSA, and the multi step predicted values are recombined to make the disturbance storm time (DST) and proton flux indexes. The proposed method is used for forecasting hourly DST index in 2001 and daily average of the DST index from 1957 to 2005 and proton flux index in 2001. The results are remarkably good in the predictions of DST and proton flux indexes.

  18. Using Fuzzy Analytic Hierarchy Process multicriteria and Geographical information system for coastal vulnerability analysis in Morocco: The case of Mohammedia

    NASA Astrophysics Data System (ADS)

    Tahri, Meryem; Maanan, Mohamed; Hakdaoui, Mustapha

    2016-04-01

    This paper shows a method to assess the vulnerability of coastal risks such as coastal erosion or submarine applying Fuzzy Analytic Hierarchy Process (FAHP) and spatial analysis techniques with Geographic Information System (GIS). The coast of the Mohammedia located in Morocco was chosen as the study site to implement and validate the proposed framework by applying a GIS-FAHP based methodology. The coastal risk vulnerability mapping follows multi-parametric causative factors as sea level rise, significant wave height, tidal range, coastal erosion, elevation, geomorphology and distance to an urban area. The Fuzzy Analytic Hierarchy Process methodology enables the calculation of corresponding criteria weights. The result shows that the coastline of the Mohammedia is characterized by a moderate, high and very high level of vulnerability to coastal risk. The high vulnerability areas are situated in the east at Monika and Sablette beaches. This technical approach is based on the efficiency of the Geographic Information System tool based on Fuzzy Analytical Hierarchy Process to help decision maker to find optimal strategies to minimize coastal risks.

  19. Analysis of the noise reduction property of type-2 fuzzy logic systems using a novel type-2 membership function.

    PubMed

    Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Teshnehlab, Mohammad; Kaynak, Okyay

    2011-10-01

    In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.

  20. Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System

    PubMed Central

    Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.

    2010-01-01

    The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

  1. International Instructional Systems: How England Measures Up

    ERIC Educational Resources Information Center

    Creese, Brian; Isaacs, Tina

    2016-01-01

    Although England was not included in the International Instructional Systems Study because it was not a high-performing jurisdiction by the Study's definition, contributors largely were England-based. Analysing the Study's nine overall aspects of instructional systems, this paper finds that England is out of step with many of the high-performing…

  2. International Systems Integration on the International Space Station

    NASA Technical Reports Server (NTRS)

    Gerstenmaier, William H.; Ticker, Ronald L.

    2007-01-01

    Over the next few months, the International Space Station (ISS), and human spaceflight in general, will undergo momentous change. The European Columbus and Japanese Kibo Laboratories will be added to the station joining U.S. and Russian elements already on orbit. Columbus, Jules Vernes Automated Transfer Vehicle (ATV) and Kibo Control Centers will soon be joining control centers in the US and Russia in coordinating ISS operations and research. The Canadian Special Purpose Dexterous Manipulator (SPDM) will be performing extra vehicular activities that previously only astronauts on EVA could do, but remotely and with increased safety. This paper will address the integration of these international elements and operations into the ISS, both from hardware and human perspectives. Interoperability of on-orbit systems and ground control centers and their human operators from Europe, Japan, Canada, Russia and the U.S. pose significant and unique challenges. Coordination of logistical support and transportation of crews and cargo is also a major challenge. As we venture out into the cosmos and inhabit the Moon and other planets, it's the systems and operational experience and partnership development on ISS, humanity's orbiting outpost that is making these journeys possible.

  3. Fuzzy Pool Balance: An algorithm to achieve a two dimensional balance in distribute storage systems

    NASA Astrophysics Data System (ADS)

    Wu, Wenjing; Chen, Gang

    2014-06-01

    The limitation of scheduling modules and the gradual addition of disk pools in distributed storage systems often result in imbalances among their disk pools in terms of both disk usage and file count. This can cause various problems to the storage system such as single point of failure, low system throughput and imbalanced resource utilization and system loads. An algorithm named Fuzzy Pool Balance (FPB) is proposed here to solve this problem. The input of FPB is the current file distribution among disk pools and the output is a file migration plan indicating what files are to be migrated to which pools. FPB uses an array to classify the files by their sizes. The file classification array is dynamically calculated with a defined threshold named Tmax that defines the allowed pool disk usage deviations. File classification is the basis of file migration. FPB also defines the Immigration Pool (IP) and Emigration Pool (EP) according to the pool disk usage and File Quantity Ratio (FQR) that indicates the percentage of each category of files in each disk pool, so files with higher FQR in an EP will be migrated to IP(s) with a lower FQR of this file category. To verify this algorithm, we implemented FPB on an ATLAS Tier2 dCache production system. The results show that FPB can achieve a very good balance in both free space and file counts, and adjusting the threshold value Tmax and the correction factor to the average FQR can achieve a tradeoff between free space and file count.

  4. Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems

    PubMed Central

    Lebar Bajec, Iztok

    2017-01-01

    Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour. PMID:28045964

  5. Novel intelligent real-time position tracking system using FPGA and fuzzy logic.

    PubMed

    Soares dos Santos, Marco P; Ferreira, J A F

    2014-03-01

    The main aim of this paper is to test if FPGAs are able to achieve better position tracking performance than software-based soft real-time platforms. For comparison purposes, the same controller design was implemented in these architectures. A Multi-state Fuzzy Logic controller (FLC) was implemented both in a Xilinx(®) Virtex-II FPGA (XC2v1000) and in a soft real-time platform NI CompactRIO(®)-9002. The same sampling time was used. The comparative tests were conducted using a servo-pneumatic actuation system. Steady-state errors lower than 4 μm were reached for an arbitrary vertical positioning of a 6.2 kg mass when the controller was embedded into the FPGA platform. Performance gains up to 16 times in the steady-state error, up to 27 times in the overshoot and up to 19.5 times in the settling time were achieved by using the FPGA-based controller over the software-based FLC controller.

  6. 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.

  7. Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.

    PubMed

    Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise

    2016-05-01

    In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment.

  8. Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

    PubMed

    Demšar, Jure; Lebar Bajec, Iztok

    2017-01-01

    Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

  9. Identification of filter management strategy in fluid power systems under uncertainty: an interval-fuzzy parameter integer nonlinear programming method

    NASA Astrophysics Data System (ADS)

    Nie, S. L.; Hu, B.; Li, Y. P.; Hu, Z.; Huang, G. H.

    2011-03-01

    An interval-fuzzy integer nonlinear programming (IFINP) method is developed for the identification of filter allocation and replacement strategies in a fluid power system (FPS) under uncertainty. It can handle uncertainties expressed as interval-fuzzy values that exist in the left- and right-hand sides of constraints as well as in the objective function. The developed method is applied to a case of planning filter allocation and replacement strategies under uncertainty for a FPS with a single circuit. A piecewise linearisation approach is used to convert the nonlinear problem of FPS into a linear one. The generated fuzzy solutions will be used to analyse and interpret the multiple decision alternatives under various system conditions, and thus help decision-makers to make a compromise among the system contamination level, system cost, satisfaction degrees and system-failure risks under different contaminant ingression/generation rates. The results demonstrate that the suction and return filters can effectively reduce the contamination level associated with a low system cost, but the FPS will take lots of failure risk when the contaminant ingression/generation rate is high; and the combination of suction and pressure filters can bring the lowest system cost with more security instead. Furthermore, comparisons for the optimised solutions are made among IFINP, interval-parameter integer nonlinear programming and deterministic linear programming also. Generally, the IFINP method can effectively reduce the total design and operation cost of the filtration system when contaminants ingression/generation rate is high, and it could be extended to the lubricating system.

  10. International collaboration in SAR ground data systems

    NASA Technical Reports Server (NTRS)

    Curlander, John C.

    1993-01-01

    A set of considerations that are pertinent to future international cooperation in the area of synthetic aperture radar (SAR) ground data systems are presented. The considerations are as follows: (1) success of future spaceborne SAR missions will require multi-agency and/or multi-national collaboration; (2) ground processing is typically performed by each agency for their user base; (3) international standards are required to achieve a uniform data product independent of the processing center; (4) to reduce the aggregate cost of the ground data systems, collaboration is required in design and development; (5) effective utilization of the data by an international user community; (6) commercialization of data products; and (7) security of data systems.

  11. A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease.

    PubMed

    Camara, Carmen; Warwick, Kevin; Bruña, Ricardo; Aziz, Tipu; del Pozo, Francisco; Maestú, Fernando

    2015-11-01

    Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7% in 70% of the patients.

  12. Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

    NASA Astrophysics Data System (ADS)

    Lin, Cheng-Jian; Lee, Chi-Yung

    2010-04-01

    This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.

  13. Construction of fuzzy interference system for generalization of geographic information - selection of road segments

    NASA Astrophysics Data System (ADS)

    Fiedukowicz, Anna

    2013-12-01

    Automation of generalization of geographic information is known as one of the biggest challenges facing modern cartography. Realization of such a process demands knowledge base which will help to decide which algorithms in which sequence should be used and how to parameterize them. Author proposes the knowledge base based on non-classical logics: rough and fuzzy. This article presents results of first trials on the fuzzy rules for realization of selection operator. Usage of fuzzy rules and linguistic variables allows better mimic the subjective character of generalization process. Test were established on the data about roads segments coming from Topographical Database (TBD) two test areas. Conducted experiment proved the possibility of utilization of fuzzy rules in the generalization of geographic information. It may be very valuable to use the idea of rough sets and reducts for selection of the attributes which are the most significant in terms of the made decision. This will be the subject of author's further research. Presented research are the initial step for creation of knowledgebase based on non-classical logic (fuzzy and rough).

  14. Expert system training and control based on the fuzzy relation matrix

    NASA Technical Reports Server (NTRS)

    Ren, Jie; Sheridan, T. B.

    1991-01-01

    Fuzzy knowledge, that for which the terms of reference are not crisp but overlapped, seems to characterize human expertise. This can be shown from the fact that an experienced human operator can control some complex plants better than a computer can. Proposed here is fuzzy theory to build a fuzzy expert relation matrix (FERM) from given rules or/and examples, either in linguistic terms or in numerical values to mimic human processes of perception and decision making. The knowledge base is codified in terms of many implicit fuzzy rules. Fuzzy knowledge thus codified may also be compared with explicit rules specified by a human expert. It can also provide a basis for modeling the human operator and allow comparison of what a human operator says to what he does in practice. Two experiments were performed. In the first, control of liquid in a tank, demonstrates how the FERM knowledge base is elicited and trained. The other shows how to use a FERM, build up from linguistic rules, and to control an inverted pendulum without a dynamic model.

  15. GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.

    PubMed

    Tung, W L; Quek, C; Cheng, P

    2004-05-01

    Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi

  16. Internally insulated thermal storage system development program

    NASA Technical Reports Server (NTRS)

    Scott, O. L.

    1980-01-01

    A cost effective thermal storage system for a solar central receiver power system using molten salt stored in internally insulated carbon steel tanks is described. Factors discussed include: testing of internal insulation materials in molten salt; preliminary design of storage tanks, including insulation and liner installation; optimization of the storage configuration; and definition of a subsystem research experiment to demonstrate the system. A thermal analytical model and analysis of a thermocline tank was performed. Data from a present thermocline test tank was compared to gain confidence in the analytical approach. A computer analysis of the various storage system parameters (insulation thickness, number of tanks, tank geometry, etc.,) showed that (1) the most cost-effective configuration was a small number of large cylindrical tanks, and (2) the optimum is set by the mechanical constraints of the system, such as soil bearing strength and tank hoop stress, not by the economics.

  17. Use of fuzzy logic in lignite inventory estimation

    SciTech Connect

    Tutmez, B.; Dag, A.

    2007-07-01

    Seam thickness is one of the most important parameters for reserve estimation of a lignite deposit. This paper addresses a case study on fuzzy estimation of lignite seam thickness from spatial coordinates. From the relationships between input (Cartesian coordinates) and output (thickness) parameters, fuzzy clustering and a fuzzy rule-based inference system were designed. Data-driven fuzzy model parameters were derived from numerical values directly. In addition, estimations of the fuzzy model were compared with kriging estimations. It was concluded that the performance ofthe fuzzy model was more satisfactory. The results indicated that the fuzzy modeling approach is very reliable for the estimation of lignite reserves.

  18. NFC internal: an indoor navigation system.

    PubMed

    Ozdenizci, Busra; Coskun, Vedat; Ok, Kerem

    2015-03-27

    Indoor navigation systems have recently become a popular research field due to the lack of GPS signals indoors. Several indoors navigation systems have already been proposed in order to eliminate deficiencies; however each of them has several technical and usability limitations. In this study, we propose NFC Internal, a Near Field Communication (NFC)-based indoor navigation system, which enables users to navigate through a building or a complex by enabling a simple location update, simply by touching NFC tags those are spread around and orient users to the destination. In this paper, we initially present the system requirements, give the design details and study the viability of NFC Internal with a prototype application and a case study. Moreover, we evaluate the performance of the system and compare it with existing indoor navigation systems. It is seen that NFC Internal has considerable advantages and significant contributions to existing indoor navigation systems in terms of security and privacy, cost, performance, robustness, complexity, user preference and commercial availability.

  19. NFC Internal: An Indoor Navigation System

    PubMed Central

    Ozdenizci, Busra; Coskun, Vedat; Ok, Kerem

    2015-01-01

    Indoor navigation systems have recently become a popular research field due to the lack of GPS signals indoors. Several indoors navigation systems have already been proposed in order to eliminate deficiencies; however each of them has several technical and usability limitations. In this study, we propose NFC Internal, a Near Field Communication (NFC)-based indoor navigation system, which enables users to navigate through a building or a complex by enabling a simple location update, simply by touching NFC tags those are spread around and orient users to the destination. In this paper, we initially present the system requirements, give the design details and study the viability of NFC Internal with a prototype application and a case study. Moreover, we evaluate the performance of the system and compare it with existing indoor navigation systems. It is seen that NFC Internal has considerable advantages and significant contributions to existing indoor navigation systems in terms of security and privacy, cost, performance, robustness, complexity, user preference and commercial availability. PMID:25825976

  20. 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.

  1. A Neuro-Fuzzy based System for Classification of Natural Textures

    NASA Astrophysics Data System (ADS)

    Jiji, G. Wiselin

    2016-12-01

    A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.

  2. A fuzzy-based shared controller for brain-actuated simulated robotic system.

    PubMed

    Liu, Rong; Xue, Kuang-Zheng; Wang, Yong-Xuan; Yang, Le

    2011-01-01

    The primary problems of brain-computer interface (BCI) are the low channel capacity and high error rate. Therefore, an assistive motion control method is important for the brain-actuated robot to realize real-time and reliable control. To make the brain-actuated robot respond to the external environments with more flexibility, a shared control method based on fuzzy logic is proposed. Experimental results obtained with ten healthy voluntary subjects show that the proposed fuzzy-based shared controller has improved performance compared with direct control approach.

  3. Cooperative fuzzy adaptive output feedback control for synchronisation of nonlinear multi-agent systems under directed graphs

    NASA Astrophysics Data System (ADS)

    Wang, W.; Wang, D.; Peng, Z. H.

    2015-12-01

    This paper considers the leader-following synchronisation problem of nonlinear multi-agent systems with unmeasurable states and a dynamic leader whose input is not available to any follower. Each follower is governed by a nonlinear system with unknown dynamics. Two distributed fuzzy adaptive protocols, based on local and neighbourhood observers, respectively, are proposed to guarantee that the states of all followers synchronise to that of the leader, under the condition that the communication graph among the followers contains a directed spanning tree. Based on Lyapunov stability theory, the synchronisation errors are guaranteed to be cooperatively uniformly ultimately bounded. Two examples are provided to show the effectiveness of the proposed controllers.

  4. FLEXnav: a fuzzy logic expert dead-reckoning system for the Segway RMP

    NASA Astrophysics Data System (ADS)

    Ojeda, Lauro; Raju, Mukunda; Borenstein, Johann

    2004-09-01

    Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, in recent years fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. A unique new mobile robot is the Segway Robotics Mobility Platform (RMP). This functionally close relative of the innovative Segway Human Transporter (HT) stabilizes a statically unstable single-axle robot dynamically, based on the principle of the inverted pendulum. While this approach works very well for human transportation, it introduces as unique set of challenges to navigation equipment using an onboard gyro. This is due to the fact that in operation the Segway RMP constantly changes its forward tilt, to prevent dynamically falling over. This paper introduces our new Fuzzy Logic Expert rule-based navigation (FLEXnav) method for fusing data from multiple gyroscopes and accelerometers in order to estimate accurately the attitude (i.e., heading and tilt) of a mobile robot. The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot. We have further extended this approach to include the special conditions of operation on the Segway RMP. The paper presents experimental results of a Segway RMP equipped with our system and running over moderately rugged terrain.

  5. Experiments on neural network architectures for fuzzy logic

    NASA Technical Reports Server (NTRS)

    Keller, James M.

    1991-01-01

    The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

  6. On designing of the driven system control of solar panels using type 2 fuzzy sliding mode control (T2FSMC)

    NASA Astrophysics Data System (ADS)

    Mardlijah, Subiono, S., Sentot D.; Efprianto, Yahya

    2016-02-01

    Collectors on the solar panel can work optimally when the collectors position perpendicular to the whole solar rays. Therefore we need a control system to control the position of the collectors always perpendicular to the sun rays. In this paper, control system T2FSMC is proposed, combined SMC, FLC and fuzzy type 2 which has a membership function more complex so as to provide an additional degree of freedom that allows uncertainty. the behavior of the control system based on T2FSMC for the driven system of solar panels was analyzed by comparing T2FSMC with FSMC and SMC methods. It can be concluded that the system controller of T2FSMC works better than the system controller of FSMC and SMC; i.e. faster response time, more robust to large and small disturbance and more robust to parameter uncertainty. However, the lacks in the system T2FSMC are taking quite a long time in computation and need fuzzy logic reasoning.

  7. Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development.

    PubMed

    Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Xiujuan; Chen, Jiapei

    2017-03-01

    Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

  8. Sensitivity of complex, internally coupled systems

    NASA Technical Reports Server (NTRS)

    Sobieszczanski-Sobieski, Jaroslaw

    1990-01-01

    A method is presented for computing sensitivity derivatives with respect to independent (input) variables for complex, internally coupled systems, while avoiding the cost and inaccuracy of finite differencing performed on the entire system analysis. The method entails two alternative algorithms: the first is based on the classical implicit function theorem formulated on residuals of governing equations, and the second develops the system sensitivity equations in a new form using the partial (local) sensitivity derivatives of the output with respect to the input of each part of the system. A few application examples are presented to illustrate the discussion.

  9. Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

    PubMed

    Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

    2016-07-01

    Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink.

  10. Modeling and Simulation of Control Actuation System with Fuzzy-PID Logic Controlled Brushless Motor Drives for Missiles Glider Applications.

    PubMed

    Muniraj, Murali; Arulmozhiyal, Ramaswamy

    2015-01-01

    A control actuation system has been used extensively in automotive, aerospace, and defense applications. The major challenges in modeling control actuation system are rise time, maximum peak to peak overshoot, and response to nonlinear system with percentage error. This paper addresses the challenges in modeling and real time implementation of control actuation system for missiles glider applications. As an alternative fuzzy-PID controller is proposed in BLDC motor drive followed by linkage mechanism to actuate fins in missiles and gliders. The proposed system will realize better rise time and less overshoot while operating in extreme nonlinear dynamic system conditions. A mathematical model of BLDC motor is derived in state space form. The complete control actuation system is modeled in MATLAB/Simulink environment and verified by performing simulation studies. A real time prototype of the control actuation is developed with dSPACE-1104 hardware controller and a detailed analysis is carried out to confirm the viability of the proposed system.

  11. Reconfigurable fuzzy cell

    NASA Technical Reports Server (NTRS)

    Salazar, George A. (Inventor)

    1993-01-01

    This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.

  12. An integrated SOM-fuzzy ARTMAP neural system for the evaluation of toxicity.

    PubMed

    Espinosa, G; Arenas, A; Giralt, Francesc

    2002-01-01

    Self-organized maps (SOM) have been applied to analyze the similarities of chemical compounds and to select from a given pool of descriptors the smallest and more relevant subset needed to build robust QSAR models based on fuzzy ARTMAP. First, the category maps for each molecular descriptor and for the target activity variable were created with SOM and then classified on the basis of topology and nonlinear distribution. The best subset of descriptors was obtained by choosing from each cluster the index with the highest correlation with the target variable and then in order of decreasing correlation. This process was terminated when a dissimilarity measure increased, indicating that the inclusion of more molecular indices would not add supplementary information. The optimal subset of descriptors was used as input to a fuzzy ARTMAP architecture modified to effect predictive capabilities. The performance of the integrated SOM-fuzzy ARTMAP approach was evaluated with the prediction of the acute toxicity LC50 of a homogeneous set of 69 benzene derivatives in the fathead minnow and the oral rat toxicity LD50 of a heterogeneous set of 155 organic compounds. The proposed methodology minimized the problem of misclassification of similar compounds and significantly enhanced the predictive capabilities of a properly trained fuzzy ARTMAP network.

  13. Adaptive fuzzy output-feedback controller design for nonlinear systems via backstepping and small-gain approach.

    PubMed

    Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip

    2014-10-01

    This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.

  14. PredyCLU: a prediction system for chronic leg ulcers based on fuzzy logic; part I - exploring the venous side.

    PubMed

    de Franciscis, Stefano; Fregola, Salvatore; Gallo, Alessandro; Argirò, Giuseppe; Barbetta, Andrea; Buffone, Gianluca; Caliò, Francesco G; De Caridi, Giovanni; Amato, Bruno; Serra, Raffaele

    2016-12-01

    Chronic leg ulcers (CLUs) are a common occurrence in the western population and are associated with a negative impact on the quality of life of patients. They also cause a substantial burden on the health budget. The pathogenesis of leg ulceration is quite heterogeneous, and chronic venous ulceration (CVU) is the most common manifestation representing the main complication of chronic venous disease (CVD). Prevention strategies and early identification of the risk represent the best form of management. Fuzzy logic is a flexible mathematical system that has proved to be a powerful tool for decision-making systems and pattern classification systems in medicine. In this study, we have elaborated a computerised prediction system for chronic leg ulcers (PredyCLU) based on fuzzy logic, which was retrospectively applied on a multicentre population of 77 patients with CVD. This evaluation system produced reliable risk score patterns and served effectively as a stratification risk tool in patients with CVD who were at the risk of developing CVUs.

  15. Fuzzy [Formula: see text] output-feedback control for the discrete-time system with channel fadings, sector nonlinearities, and randomly occurring interval delays and nonlinearities.

    PubMed

    Fan, Xiaozheng; Wang, Yan; Hu, Manfeng

    2016-01-01

    In this paper, the fuzzy [Formula: see text] output-feedback control problem is investigated for a class of discrete-time T-S fuzzy systems with channel fadings, sector nonlinearities, randomly occurring interval delays (ROIDs) and randomly occurring nonlinearities (RONs). A series of variables of the randomly occurring phenomena obeying the Bernoulli distribution is used to govern ROIDs and RONs. Meanwhile, the measurement outputs are subject to the sector nonlinearities (i.e. the sensor saturations) and we assume the system output is [Formula: see text], [Formula: see text]. The Lth-order Rice model is utilized to describe the phenomenon of channel fadings by setting different values of the channel coefficients. The aim of this work is to deal with the problem of designing a full-order dynamic fuzzy [Formula: see text] output-feedback controller such that the fuzzy closed-loop system is exponentially mean-square stable and the [Formula: see text] performance constraint is satisfied, by means of a combination of Lyapunov stability theory and stochastic analysis along with LMI methods. The proposed fuzzy controller parameters are derived by solving a convex optimization problem via the semidefinite programming technique. Finally, a numerical simulation is given to illustrate the feasibility and effectiveness of the proposed design technique.

  16. Valve control system for internal combustion engines

    SciTech Connect

    Kaptur, S.J.

    1989-10-24

    This patent describes a valve control system for an internal combustion engine. The system comprising a primary control and a secondary control for modifying the operation of the primary control. The primary control comprising: a camshaft journaled for rotation in camshaft brackets, intake and exhaust cylindrical cams including cam channels; valve pin means; and timing belt means. The secondary system comprising: control plate means adjustably mounted between the cylindrical cams, rocker arm means; and at least one driver positioned between the driver leg and one of cylindrical cams.

  17. Fuzzy resource optimization for safeguards

    SciTech Connect

    Zardecki, A.; Markin, J.T.

    1991-01-01

    Authorization, enforcement, and verification -- three key functions of safeguards systems -- form the basis of a hierarchical description of the system risk. When formulated in terms of linguistic rather than numeric attributes, the risk can be computed through an algorithm based on the notion of fuzzy sets. Similarly, this formulation allows one to analyze the optimal resource allocation by maximizing the overall detection probability, regarded as a linguistic variable. After summarizing the necessary elements of the fuzzy sets theory, we outline the basic algorithm. This is followed by a sample computation of the fuzzy optimization. 10 refs., 1 tab.

  18. Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System

    PubMed Central

    Krasteva, Vessela; Jekova, Irena; Leber, Remo; Schmid, Ramun; Abächerli, Roger

    2015-01-01

    This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules. PMID:26461492

  19. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  20. Closed-form estimates of the domain of attraction for nonlinear systems via fuzzy-polynomial models.

    PubMed

    Pitarch, José Luis; Sala, Antonio; Ariño, Carlos Vicente

    2014-04-01

    In this paper, the domain of attraction of the origin of a nonlinear system is estimated in closed form via level sets with polynomial boundaries, iteratively computed. In particular, the domain of attraction is expanded from a previous estimate, such as a classical Lyapunov level set. With the use of fuzzy-polynomial models, the domain of attraction analysis can be carried out via sum of squares optimization and an iterative algorithm. The result is a function that bounds the domain of attraction, free from the usual restriction of being positive and decrescent in all the interior of its level sets.

  1. FN-DFE: fuzzy-neural data fusion engine for enhanced resilient state-awareness of hybrid energy systems.

    PubMed

    Wijayasekara, Dumidu; Linda, Ondrej; Manic, Milos; Rieger, Craig

    2014-11-01

    Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzy-neural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory's hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

  2. FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced State-Awareness of Resilient Hybrid Energy System

    SciTech Connect

    Ondrej Linda; Dumidu Wijayasekara; Milos Manic; Craig Rieger

    2014-11-01

    Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a Fuzzy-Neural Data Fusion Engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms, 2) anomalous behavior detector using self-organizing fuzzy logic system, and 3) artificial neural network based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory’s (INL) hybrid energy system facility know as HYTEST. Experimental results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold based alarm systems.

  3. Robust dissipative filtering for a kind of T-S fuzzy descriptor system with immeasurable premise variables

    NASA Astrophysics Data System (ADS)

    Zhu, Baoyan; Zhang, Qingling; Zhao, Enliang

    2016-01-01

    The problem of delay-dependent robust dissipative filtering is investigated for a kind of Takagi-Sugeno (T-S) fuzzy descriptor system with immeasurable premise variables. By utilising the free-weighting matrix approach and combining them with the structural characteristics of the error system, we propose the solvable conditions of the dissipative filter that ensure an error system with immeasurable states is admissible and strictly dissipative. This implies that it is not necessary to assume that the error systems are regular and impulse-free prior to designing filters. The derived method can be applied broadly to nonlinear systems. Also, the solvable condition of the dissipative filter with measurable states is a special case of this study. We also elicit the design methods of the H∞ and passive filters, which could potentially reduce the cost and time spent on the filter design. Finally, we perform simulations to validate the derived methods for two kinds of nonlinear descriptor systems.

  4. The Bosnian War Crimes Trial Simulation: Teaching Students about the Fuzziness of World Politics and International Law.

    ERIC Educational Resources Information Center

    Jefferson, Kurt W.

    1999-01-01

    Explains using the Bosnian war crimes simulation to introduce international law and its political and legal ramifications in the course "Introduction to International Politics." Discusses the characteristics of the simulation, its effectiveness, and the response by students. Offers an evaluation of the exercise. (CMK)

  5. Using adaptive neuro-fuzzy inference system technique for crosstalk correction in simultaneous 99mTc/201Tl SPECT imaging: A Monte Carlo simulation study

    NASA Astrophysics Data System (ADS)

    Heidary, Saeed; Setayeshi, Saeed

    2015-01-01

    This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous 99mTc/201Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of 201Tl (77±10% keV) and 99mTc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.

  6. 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.

  7. M-matrix based robust stability and stabilization for uncertain discrete-time switched TS fuzzy systems with time-varying delays.

    PubMed

    Jaballi, Ahmed; Sakly, Anis; Hajjaji, Ahmed El

    2016-07-01

    This paper provides novel sufficient conditions on robust asymptotic stability and stabilization for a class of uncertain discrete-time switched fuzzy with time-varying delays. The attention is focused on developing new algebraic criteria to break with classical criteria in terms of Linear Matrix Inequalities (LMIs). Firstly, based on the M-matrix proprieties and through l1,∞ induced norms notion, new delay-dependent sufficient conditions are derived to ensure the asymptotic stability and stabilization for a class of uncertain discrete-time switched fuzzy systems with time-varying delay. Secondly, these results are extended for a class of uncertain discrete-time switched fuzzy systems with time delays, modeled by difference equations. Finally, two numerical examples and practical example (a robot arm) are provided to demonstrate the advantage and the effectiveness of our results.

  8. Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient.

    PubMed

    Chowdhury, Shubhajit Roy; Saha, Hiranmay

    2010-02-01

    The paper describes the design and training of a fuzzy neural network used for early diagnosis of a patient through an FPGA based implementation of a smart instrument. The system employs a fuzzy interface cascaded with a feed-forward neural network. In order to obtain an optimum decision regarding the future pathophysiological state of a patient, the optimal weights of the synapses between the neurons have been determined by using inverse delayed function model of neurons. The neurons that are considered in the proposed network are devoid of self connections instead of commonly used self connected neurons. The current work also find out the optimal number of neurons in the hidden layer for accurate diagnosis as against the available number of CLB in the FPGA. The system has been trained and tested with renal data of patients taken at 10 days interval of time. Applying the methodology, the chance of attainment of critical renal condition of a patient has been predicted with an accuracy of 95.2%, 30 days ahead of actually attaining the critical condition. The system has also been tested for pathophysiological state prediction of patients at multiple time steps ahead and the prediction at the next instant of time stands out to be the most accurate.

  9. Reliable Output Feedback Control for T-S Fuzzy Systems With Decentralized Event Triggering Communication and Actuator Failures.

    PubMed

    Zhang, Changzhu; Hu, Jinfei; Qiu, Jianbin; Chen, Qijun

    2017-02-23

    Due to the unavailability of full state variables in many control systems, this paper is concerned with the design of reliable observer-based output feedback controller for a class of network-based Takagi-Sugeno fuzzy systems with actuator failures. In order to better allocate network resources under the case that the sensor nodes are physically distributed, the decentralized event triggering communication scheme is adopted such that each sensor node is capable to determine the transmission of its local measurement information independently. Considering that the implementation of the controller may not be synchronized with the plant trajectories due to asynchronous premise variables with such communication mechanism, a novel piecewise fuzzy observer-based output feedback controller is developed. By applying a piecewise Lyapunov function and some techniques on matrix convexification, an approach to the design of observer and controller gain is derived for the augmented closed-loop system to be asymptotically stable with a guaranteed H∞ performance and reduced transmission frequency. Finally, two examples are given to show the effectiveness of the developed method.

  10. The evolution of the international refugee system.

    PubMed

    Gallagher, D

    1989-01-01

    This article examines the evolution of the current international system for responding to refugee problems and the climate within which the legal and institutional framework has developed. It reviews the background and handling of some of the key refugee movements since World War II and traces the legal and institutional adjustments that have been made to deal with new refugee movements that have occurred predominantly, but not exclusively, in the developing world. Finally, it assesses the adequacy of the present system to meet the challenges ahead.

  11. Models of neural networks with fuzzy activation functions

    NASA Astrophysics Data System (ADS)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  12. Fuzzy jets

    SciTech Connect

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad

    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 variables 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.

  13. 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

  14. A fault-tolerant attitude control system for a satellite based on fuzzy global sliding mode control algorithm

    NASA Astrophysics Data System (ADS)

    Liang, Jinjin; Dong, Chaoyang; Wang, Qing

    2008-10-01

    The structures and missions of modern satellites are very complicated, so the reliability of satellites is becoming increasingly important. This paper proposed a fault-tolerant attitude control system for a satellite based on Fuzzy Global Sliding Mode Control (FGSMC) algorithm. We designed a controller for the nonlinear model of a satellite. By designing a global sliding surface, this controller can ensure that the response of the system has global robustness against the uncertainties of system and external disturbances. In this paper attitude control is performed by four reaction flywheels. The attitude control system distributed the three control torques to the four reaction flywheels according to the distribution matrix. We deduced the formula to calculate the distribution matrix. Paper proved the stability of the designed control law, and simulated the attitude control system. The simulation results show that the attitude control law has high accuracy and robustness.

  15. Identification of complex systems based on neural and Takagi-Sugeno fuzzy model.

    PubMed

    Kukolj, Dragan; Levi, Emil

    2004-02-01

    The paper describes a neuro-fuzzy identification approach, which uses numerical data as a starting point. The proposed method generates a Takagi-Sugeno fuzzy model, characterized with transparency, high accuracy and a small number of rules. The process of self-organizing of the identification model consists of three phases: clustering of the input-output space using a self-organized neural network; determination of the parameters of the consequent part of a rule from over-determined batch least-squares formulation of the problem, using singular value decomposition algorithm; and on-line adaptation of these parameters using recursive least-squares method. The verification of the proposed identification approach is provided using four different problems: two benchmark identification problems, speed estimation for a dc motor drive, and estimation of the temperature in a tunnel furnace for clay baking.

  16. Automated nonlinear system modeling with multiple fuzzy neural networks and kernel smoothing.

    PubMed

    Yu, Wen; Li, Xiaoou

    2010-10-01

    This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

  17. Clustering of noisy image data using an adaptive neuro-fuzzy system

    NASA Technical Reports Server (NTRS)

    Pemmaraju, Surya; Mitra, Sunanda

    1992-01-01

    Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.

  18. An application of adaptive neuro-fuzzy inference system to landslide susceptibility mapping (Klang valley, Malaysia)

    NASA Astrophysics Data System (ADS)

    Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan

    2010-05-01

    Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient

  19. An Internal Coaxil Cable Seal System

    DOEpatents

    Hall, David R.; Hall, Jr., H. Tracy; Pixton, David; Dahlgren, Scott; Sneddon, Cameron; Briscoe, Michael; Fox, Joe

    2004-12-23

    The invention is a seal system for a coaxial cable more specifically an internal seal system placed within the coaxial cable and its constituent components. A series of seal stacks including flexible rigid rings and elastomeric rings are placed on load bearing members within the coaxial cable. The current invention is adapted to seal the annular space between the coaxial cable and an electrical contact passing there through. The coaxial cable is disposed within drilling components to transmit electrical signals between drilling components within a drill string. During oil and gas exploration, a drill string can see a range of pressures and temperatures thus resulting in multiple combinations of temperature and pressure and increasing the difficulty of creating a robust seal for all combinations. The seal system can be used in a plurality of downhole components, such as sections of pipe in a drill string, drill collars, heavy weight drill pipe, and jars.

  20. 46 CFR 184.602 - Internal communications systems.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 46 Shipping 7 2013-10-01 2013-10-01 false Internal communications systems. 184.602 Section 184.602... TONS) VESSEL CONTROL AND MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 184.602 Internal communications systems. (a) A vessel equipped with pilothouse control must...

  1. 46 CFR 184.602 - Internal communications systems.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 46 Shipping 7 2012-10-01 2012-10-01 false Internal communications systems. 184.602 Section 184.602... TONS) VESSEL CONTROL AND MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 184.602 Internal communications systems. (a) A vessel equipped with pilothouse control must...

  2. 46 CFR 121.602 - Internal communications systems.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 46 Shipping 4 2013-10-01 2013-10-01 false Internal communications systems. 121.602 Section 121.602... MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 121.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have a fixed means of...

  3. 46 CFR 184.602 - Internal communications systems.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 46 Shipping 7 2014-10-01 2014-10-01 false Internal communications systems. 184.602 Section 184.602... TONS) VESSEL CONTROL AND MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 184.602 Internal communications systems. (a) A vessel equipped with pilothouse control must...

  4. 46 CFR 121.602 - Internal communications systems.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 46 Shipping 4 2012-10-01 2012-10-01 false Internal communications systems. 121.602 Section 121.602... MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 121.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have a fixed means of...

  5. 46 CFR 121.602 - Internal communications systems.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 46 Shipping 4 2014-10-01 2014-10-01 false Internal communications systems. 121.602 Section 121.602... MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 121.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have a fixed means of...

  6. 46 CFR 121.602 - Internal communications systems.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 121.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have a fixed means of two-way... 46 Shipping 4 2011-10-01 2011-10-01 false Internal communications systems. 121.602 Section...

  7. 46 CFR 184.602 - Internal communications systems.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... TONS) VESSEL CONTROL AND MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 184.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have... 46 Shipping 7 2011-10-01 2011-10-01 false Internal communications systems. 184.602 Section...

  8. 12 CFR 609.940 - Internal systems and controls.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... 12 Banks and Banking 6 2011-01-01 2011-01-01 false Internal systems and controls. 609.940 Section... for Boards and Management § 609.940 Internal systems and controls. When applicable, internal systems and controls must provide reasonable assurances that System institutions will: (a) Follow and...

  9. 46 CFR 184.602 - Internal communications systems.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... TONS) VESSEL CONTROL AND MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 184.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have... 46 Shipping 7 2010-10-01 2010-10-01 false Internal communications systems. 184.602 Section...

  10. 46 CFR 121.602 - Internal communications systems.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... MISCELLANEOUS SYSTEMS AND EQUIPMENT Control and Internal Communications Systems § 121.602 Internal communications systems. (a) A vessel equipped with pilothouse control must have a fixed means of two-way... 46 Shipping 4 2010-10-01 2010-10-01 false Internal communications systems. 121.602 Section...

  11. 12 CFR 609.940 - Internal systems and controls.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Internal systems and controls. 609.940 Section... for Boards and Management § 609.940 Internal systems and controls. When applicable, internal systems and controls must provide reasonable assurances that System institutions will: (a) Follow and...

  12. Knowledge representation in fuzzy logic

    NASA Technical Reports Server (NTRS)

    Zadeh, Lotfi A.

    1989-01-01

    The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.

  13. Adaptive fuzzy decentralized control for large-scale nonlinear systems with time-varying delays and unknown high-frequency gain sign.

    PubMed

    Tong, Shaocheng; Liu, Changliang; Li, Yongming; Zhang, Huaguang

    2011-04-01

    In this paper, an adaptive fuzzy decentralized robust output feedback control approach is proposed for a class of large-scale strict-feedback nonlinear systems without the measurements of the states. The nonlinear systems in this paper are assumed to possess unstructured uncertainties, time-varying delays, and unknown high-frequency gain sign. Fuzzy logic systems are used to approximate the unstructured uncertainties, K-filters are designed to estimate the unmeasured states, and a special Nussbaum gain function is introduced to solve the problem of unknown high-frequency gain sign. Combining the backstepping technique with adaptive fuzzy control theory, an adaptive fuzzy decentralized robust output feedback control scheme is developed. In order to obtain the stability of the closed-loop system, a new lemma is given and proved. Based on this lemma and Lyapunov-Krasovskii functions, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded and that the tracking errors can converge to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated from simulation results.

  14. A hybrid clustering based fuzzy structure for vibration control - Part 2: An application to semi-active vehicle seat-suspension system

    NASA Astrophysics Data System (ADS)

    Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

    2015-05-01

    This work presents a novel neuro-fuzzy controller (NFC) for car-driver's seat-suspension system featuring magnetorheological (MR) dampers. The NFC is built based on the algorithm for building adaptive neuro-fuzzy inference systems (ANFISs) named B-ANFIS, which has been developed in Part 1, and fuzzy logic inference systems (FISs). In order to create the NFC, the following steps are performed. Firstly, a control strategy based on a ride-comfort-oriented tendency (RCOT) is established. Subsequently, optimal FISs are built based on a genetic algorithm (GA) to estimate the desired damping force that satisfies the RCOT corresponding to the road status at each time. The B-ANFIS is then used to build ANFISs for inverse dynamic models of the suspension system (I-ANFIS). Based on the FISs, the desired force values are calculated according to the status of road at each time. The corresponding exciting current value to be applied to the MR damper is then determined by the I-ANFIS. In order to validate the effectiveness of the developed neuro-fuzzy controller, control performances of the seat-suspension systems featuring MR dampers are evaluated under different road conditions. In addition, a comparative work between conventional skyhook controller and the proposed NFC is undertaken in order to demonstrate superior control performances of the proposed methodology.

  15. 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

  16. Analysis of inventory difference using fuzzy controllers

    SciTech Connect

    Zardecki, A.

    1994-08-01

    The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented.

  17. Optical generation of fuzzy-based rules.

    PubMed

    Gur, Eran; Mendlovic, David; Zalevsky, Zeev

    2002-08-10

    In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.

  18. Optical Generation of Fuzzy-Based Rules

    NASA Astrophysics Data System (ADS)

    Gur, Eran; Mendlovic, David; Zalevsky, Zeev

    2002-08-01

    In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.

  19. Refining fuzzy logic controllers with machine learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  20. Fuzzy Q-Learning for Generalization of Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.