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.
Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.
Azar, Ahmad Taher
2013-09-01
Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679
NASA Astrophysics Data System (ADS)
Mekanik, F.; Imteaz, M. A.; Talei, A.
2016-05-01
Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill
NASA Astrophysics Data System (ADS)
Akhoondzadeh, M.
2013-09-01
Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.
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.
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.
Research on target recognition techniques of radar networking based on fuzzy mathematics
NASA Astrophysics Data System (ADS)
Guan, Chengbin; Wang, Guohong; Guan, Chengzhun; Pan, Jinshan
2007-11-01
Nowadays there are more and more targets, so it is more difficult for radar networking to track the important targets. To reduce the pressure on radar networking and the waste of ammunition, it is very necessary for radar networking to recognize the targets. Two target recognition approaches of radar networking based on fuzzy mathematics are proposed in this paper, which are multi-level fuzzy synthetical evaluation technique and lattice approaching degree technique. By analyzing the principles, the application techniques are given, the merits and shortcomings are also analyzed, and applying environments are advised. Another emphasis is the compare between the multiple mono-level fuzzy synthetical evaluation and the multi-level fuzzy synthetical evaluation, an instance is carried out to illuminate the problem, then the results are analyzed in theory, the conclusions are gotten which can be instructions for application in engineering.
Network-Based Inference Methods for Drug Repositioning
Zhang, Heng; Cao, Yiqin; Tang, Wenliang
2015-01-01
Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies. PMID:25969690
Bayesian inference with adaptive fuzzy priors and likelihoods.
Osoba, Osonde; Mitaim, Sanya; Kosko, Bart
2011-10-01
Fuzzy rule-based systems can approximate prior and likelihood probabilities in Bayesian inference and thereby approximate posterior probabilities. This fuzzy approximation technique allows users to apply a much wider and more flexible range of prior and likelihood probability density functions than found in most Bayesian inference schemes. The technique does not restrict the user to the few known closed-form conjugacy relations between the prior and likelihood. It allows the user in many cases to describe the densities with words and just two rules can absorb any bounded closed-form probability density directly into the rulebase. Learning algorithms can tune the expert rules as well as grow them from sample data. The learning laws and fuzzy approximators have a tractable form because of the convex-sum structure of additive fuzzy systems. This convex-sum structure carries over to the fuzzy posterior approximator. We prove a uniform approximation theorem for Bayesian posteriors: An additive fuzzy posterior uniformly approximates the posterior probability density if the prior or likelihood densities are continuous and bounded and if separate additive fuzzy systems approximate the prior and likelihood densities. Simulations demonstrate this fuzzy approximation of priors and posteriors for the three most common conjugate priors (as when a beta prior combines with a binomial likelihood to give a beta posterior). Adaptive fuzzy systems can also approximate non-conjugate priors and likelihoods as well as approximate hyperpriors in hierarchical Bayesian inference. The number of fuzzy rules can grow exponentially in iterative Bayesian inference if the previous posterior approximator becomes the new prior approximator. PMID:21478078
Fuzzy exemplar-based inference system for flood forecasting
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin
2005-02-01
Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on a fuzzy inference system, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.
Streamflow Forecasting Using Nuero-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Nanduri, U. V.; Swain, P. C.
2005-12-01
Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.
Inference of Gene Regulatory Network Based on Local Bayesian Networks.
Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan
2016-08-01
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce
Inference of Gene Regulatory Network Based on Local Bayesian Networks
Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Chen, Luonan
2016-01-01
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce
Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning.
Er, Meng Joo; Deng, Chang
2004-06-01
This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior. PMID:15484918
Automatic generation of fuzzy inference systems via unsupervised learning.
Er, Meng Joo; Zhou, Yi
2008-12-01
In this paper, a novel approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating Fuzzy Inference Systems (FISs) is presented. In the EDSGFQL approach, structure identification and parameter estimations of FISs are achieved via Unsupervised Learning (UL) (including Reinforcement Learning (RL)). Instead of using Supervised Learning (SL), UL clustering methods are adopted for input space clustering when generating FISs. At the same time, structure and preconditioning parts of a FIS are generated in a RL manner in that fuzzy rules are adjusted and deleted according to reinforcement signals. The proposed EDSGFQL methodologies can automatically create, delete and adjust fuzzy rules dynamically. Simulation studies on wall-following and obstacle avoidance tasks by a mobile robot show that the proposed approach is superior in generating efficient FISs. PMID:18653313
Fuzzy inference of soil patterns: Implications for watershed modeling
NASA Astrophysics Data System (ADS)
Zhu, A.-Xing
A fuzzy inference scheme for inferring and representing detailed soil spatial information is first reviewed. This scheme consists of two major components: a fuzzy logic-based model (called a similarity model) and a set of inference techniques. Under the similarity model the soil landscape is perceived as a continuum in both its parameter space and geographic space so that detailed spatial variation of soil information can be represented. A set of inference techniques were used to populate this similarity model by combining the knowledge of soil experts with data on soil formative environment. The provision of this detailed soil spatial information represented under this similarity model has significant implications for watershed modeling, particularly for solute transport modeling in the vadose zone. It was found that the characterization of a key soil parameter (solum depth) based on the soil spatial information derived from the fuzzy inference scheme differs significantly from that derived from the conventional soil map for watershed modeling using a lumped-parameter approach. It was also found that the provision of this detailed soil spatial information allows the realistic characterization of the spatial covariation of landscape parameters for distributed modeling at the watershed scale. Specifically, the within-land-unit variation of solum depth is more realistically characterized using the inferred soil spatial information than using the conventional soil map.
Diagnosis of arthritis through fuzzy inference system.
Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet
2012-06-01
Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process. PMID:20927572
Seizure prediction using adaptive neuro-fuzzy inference system.
Rabbi, Ahmed F; Azinfar, Leila; Fazel-Rezai, Reza
2013-01-01
In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. PMID:24110134
Structure identification in fuzzy inference using reinforcement learning
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.; Khedkar, Pratap
1993-01-01
In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.
Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images
NASA Astrophysics Data System (ADS)
Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.
1996-06-01
This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.
Prediction of drug-target interactions and drug repositioning via network-based inference.
Cheng, Feixiong; Liu, Chuang; Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun
2012-01-01
Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun
2012-01-01
Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709
On the functional equivalence of fuzzy inference systems and spline-based networks.
Hunt, K J; Haas, R; Brown, M
1995-06-01
The conditions under which spline-based networks are functionally equivalent to the Takagi-Sugeno-model of fuzzy inference are formally established. We consider a generalized form of basis function network whose basis functions are splines. The result admits a wide range of fuzzy membership functions which are commonly encountered in fuzzy systems design. We use the theoretical background of functional equivalence to develop a hybrid fuzzy-spline net for inverse dynamic modeling of a hydraulically driven robot manipulator. PMID:7496588
An evolutionary approach toward dynamic self-generated fuzzy inference systems.
Zhou, Yi; Er, Meng Joo
2008-08-01
An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods. PMID:18632385
Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic
NASA Technical Reports Server (NTRS)
Lara-Rosano, Felipe
1992-01-01
In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.
NASA Astrophysics Data System (ADS)
Aminifar, S.; Yosefi, Gh.
2007-09-01
In this paper, we present away of using Anfis architecture to implement a new fuzzy logic controller chip. Anfis which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data makes fuzzy system to learn. This training is given from a standard response of the system and membership functions are suitably modified. For adaptive Anfis based fuzzy controller and its circuit design, we propose new circuits for implementing each controller block, and illustrate the test results and control surface of Anfis controller along with CMOS fuzzy logic controller using Matlab and Hspice software respectively. For implementing controller according to the Anfis training, we proposed new and improved integrated circuits which consist of Fuzzifier, Min operator and Multiplier/Divider. The control surfaces of controller are obtained by using Anfis training and simulation results of integrated circuits in less than 0.075 mm2 area in 0.35 μm CMOS standard technology.
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. PMID:25222965
Simulation of worms transmission in computer network based on SIRS fuzzy epidemic model
NASA Astrophysics Data System (ADS)
Darti, I.; Suryanto, A.; Yustianingsih, M.
2015-03-01
In this paper we study numerically the behavior of worms transmission in a computer network. The model of worms transmission is derived by modifying a SIRS epidemic model. In this case, we consider that the transmission rate, recovery rate and rate of susceptible after recovery follows fuzzy membership functions, rather than constants. To study the transmission of worms in a computer network, we solve the model using the fourth order Runge-Kutta method. Our numerical results show that the fuzzy transmission rate and fuzzy recovery rate may lead to a changing of basic reproduction number which therefore also changes the stability properties of equilibrium points.
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.
Classification of Microarray Data Using Kernel Fuzzy Inference System
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.
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.
Stereo viewing 3-component, planar PIV utilizing fuzzy inference
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
1996-01-01
An all electronic 3-D Digital Particle Image Velocimetry (DPIV) system has been developed for use in high velocity (supersonic) flows. Two high resolution CCD cameras mounted in a stereo viewing configuration are used to determine the out-of-plane velocity component from the difference of the in-plane velocity measurements. Double exposure image frames are acquired and Fuzzy inference techniques are used to maximize the validity of the velocity estimates obtained from the auto-correlation analysis. The CCD cameras are tilted relative to their respective lens axes to satisfy Scheimpflug's condition. Tilting the camera film plane ensures that the entire image plane is in focus. Perspective distortion still results, but can be corrected by proper calibration of the optical system. A calibration fixture is used to determine the experimental setup parameters and to assess the accuracy to which the z-plane displacements can be estimated. The details of the calibration fixture and procedure are discussed in the text. A pair of pulsed Nd:YAG lasers operating at 532 nm are used to illuminate the seeded flow from a convergent nozzle operated in an underexpanded condition. The light sheet was oriented perpendicular to the nozzle flow, yielding planar cross-sections of the 3-component velocity field at several axial stations. The key features of the supersonic jet are readily observed in the cross-plane vector plots.
Automated interpretation of LIBS spectra using a fuzzy logic inference engine.
Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R
2012-03-01
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment. PMID:22410914
NASA Astrophysics Data System (ADS)
Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad
2016-05-01
Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert
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.
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.
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.
Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions
NASA Astrophysics Data System (ADS)
Khoury, Mehdi; Liu, Honghai
This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
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
Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli
2016-02-01
Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. PMID:26224125
INFERRING FUNCTIONAL NETWORK-BASED SIGNATURES VIA STRUCTURALLY-WEIGHTED LASSO MODEL
Zhu, Dajiang; Shen, Dinggang; Liu, Tianming
2014-01-01
Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature. PMID:25002915
Search by Fuzzy Inference in a Children's Dictionary
ERIC Educational Resources Information Center
St-Jacques, Claude; Barriere, Caroline
2005-01-01
This research aims at promoting the usage of an online children's dictionary within a context of reading comprehension and vocabulary acquisition. Inspired by document retrieval approaches developed in the area of information retrieval (IR) research, we adapt a particular IR strategy, based on fuzzy logic, to a search in the electronic dictionary.…
Fuzzy inference game approach to uncertainty in business decisions and market competitions.
Oderanti, Festus Oluseyi
2013-01-01
The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments. PMID:24109562
An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method
NASA Astrophysics Data System (ADS)
Seki, Hirosato; Mizuguchi, Fuhito; Watanabe, Satoshi; Ishii, Hiroaki; Mizumoto, Masaharu
The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a “kernel-type SIRMs method” which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.
NASA Astrophysics Data System (ADS)
Liu, Yu; Huang, Hong-Zhong; Ling, Dan
2013-03-01
Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.
Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data
Yang, Bo; Zhang, Junying; Yin, Yaling; Zhang, Yuanyuan
2013-01-01
Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso), the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes. PMID:24073403
Real-time fuzzy inference based robot path planning
NASA Technical Reports Server (NTRS)
Pacini, Peter J.; Teichrow, Jon S.
1990-01-01
This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.
Design of a Software Sensor for Feedwater Flow Measurement Using a Fuzzy Inference System
Na, Man Gyun; Shin, Sun Ho; Jung, Dong Won
2005-06-15
Venturi meters are used to measure the feedwater flow rate in most current pressurized water reactors. These meters can decrease the thermal performance of nuclear power plants because the feedwater flow rate can be overmeasured due to their fouling phenomena that make corrosion products caused by long-term operation accumulate in the feedwater flow meters. Therefore, in this paper, a software sensor using a fuzzy inference system is developed in order to increase the thermal efficiency by accurately estimating online the feedwater flow rate. The fuzzy inference system to be used for black-box modeling of the feedwater system is equipped with an automatic design algorithm that automates the selection of the input signals to the fuzzy inference system and its fuzzy rule generation including parameter optimization. The proposed algorithm was verified by using the numerical simulation data of the MARS code for Kori Nuclear Power Plant Unit 1 and also the real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations using numerical simulation data and real plant data, the relative 2{sigma} errors and the relative maximum error are small enough. The proposed method can be applied successfully to validate and monitor the existing feedwater flow meters.
Automated Interpretation of LIBS Spectra using a Fuzzy Logic Inference Engine
Jeremy J. Hatch; Timothy R. McJunkin; Cynthia Hanson; Jill R. Scott
2012-02-01
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. A fuzzy logic inference engine (FLIE) was used to differentiate between various copper containing and stainless steel alloys as well as unknowns. Results using FLIE indicate a high degree of confidence in spectral assignment.
State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference
Al Qazlan, Tuqyah Abdullah; Kara-Mohamed, Chafia
2015-01-01
To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework. PMID:25879048
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.
Estimating daily pan evaporation using adaptive neural-based fuzzy inference system
NASA Astrophysics Data System (ADS)
Keskin, M. Erol; Terzi, Özlem; Taylan, Dilek
2009-09-01
Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake Eğirdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.
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. PMID:22752811
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
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
FCMAC-Yager: a novel Yager-inference-scheme-based fuzzy CMAC.
Sim, J; Tung, W L; Quek, Chai
2006-11-01
The cerebellum is a brain region important for a number of motor and cognitive functions. It is able to generate error correction signals to drive learning and for the acquisition of memory skills. The cerebellar model articulation controller (CMAC) is a neural network inspired by the neurophysiologic theory of the cerebellum and is recognized for its localized generalization and rapid algorithmic computation capabilities. The main deficiencies in the basic CMAC structure are: (1) it is difficult to interpret the internal operations of the CMAC network and (2) the resolution (quantization) problem arising from the partitioning of the input training space. These limitations lead to the synthesis of a fuzzy quantization technique and the mapping of a fuzzy inference scheme onto the CMAC structure. The discrete incremental clustering (DIC) technique is employed to alleviate the quantization problem in the CMAC structure, resulting in the fuzzy CMAC (FCMAC) network. The Yager inference scheme (Yager), which possesses firm fuzzy logic foundation and maps closely to the logical implication operations in the classical (binary) logic framework, is subsequently mapped onto the FCMAC structure. This results in a novel fuzzy neural architecture known as thefuzzy cerebellar model articulation controller-Yager (FCMAC-Yager) system. The proposed FCMAC-Yager network exhibits learning and memory capabilities of the cerebellum through the CMAC structure while emulating the human way of reasoning through the Yager. The new FCMAC-Yager network employs a two-phase training algorithm consisting of structural learning based on the DIC technique and parameter learning using hebbian learning (associative long-term potentiation). The proposed FCMAC-Yager architecture is evaluated using an extensive suite of real-life applications such as highway traffic-trend modeling and prediction and performing as an early warning system for bank failure classification and medical diagnosis of breast
AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.
Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying
2015-01-01
Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280
AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection
Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying
2015-01-01
Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280
A 3-D active shape model driven by fuzzy inference: application to cardiac CT and MR.
van Assen, Hans C; Danilouchkine, Mikhail G; Dirksen, Martijn S; Reiber, Johan H C; Lelieveldt, Boudewijn P F
2008-09-01
Manual quantitative analysis of cardiac left ventricular function using Multislice CT and MR is arduous because of the large data volume. In this paper, we present a 3-D active shape model (ASM) for semiautomatic segmentation of cardiac CT and MR volumes, without the requirement of retraining the underlying statistical shape model. A fuzzy c-means based fuzzy inference system was incorporated into the model. Thus, relative gray-level differences instead of absolute gray values were used for classification of 3-D regions of interest (ROIs), removing the necessity of training different models for different modalities/acquisition protocols. The 3-D ASM was evaluated using 25 CT and 15 MR datasets. Automatically generated contours were compared to expert contours in 100 locations. For CT, 82.4% of epicardial contours and 74.1% of endocardial contours had a maximum error of 5 mm along 95% of the contour arc length. For MR, those numbers were 93.2% (epicardium) and 91.4% (endocardium). Volume regression analysis revealed good linear correlations between manual and semiautomatic volumes, r(2) >/= 0.98. This study shows that the fuzzy inference 3-D ASM is a robust promising instrument for semiautomatic cardiac left ventricle segmentation. Without retraining its statistical shape component, it is applicable to routinely acquired CT and MR studies. PMID:18779074
Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.
Ibrahim, Sulaimon; Chowriappa, Pradeep; Dua, Sumeet; Acharya, U Rajendra; Noronha, Kevin; Bhandary, Sulatha; Mugasa, Hatwib
2015-12-01
Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%. PMID:26109519
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.
Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.
Ubeyli, Elif Derya
2009-10-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261
Approximation Of Multi-Valued Inverse Functions Using Clustering And Sugeno Fuzzy Inference
NASA Technical Reports Server (NTRS)
Walden, Maria A.; Bikdash, Marwan; Homaifar, Abdollah
1998-01-01
Finding the inverse of a continuous function can be challenging and computationally expensive when the inverse function is multi-valued. Difficulties may be compounded when the function itself is difficult to evaluate. We show that we can use fuzzy-logic approximators such as Sugeno inference systems to compute the inverse on-line. To do so, a fuzzy clustering algorithm can be used in conjunction with a discriminating function to split the function data into branches for the different values of the forward function. These data sets are then fed into a recursive least-squares learning algorithm that finds the proper coefficients of the Sugeno approximators; each Sugeno approximator finds one value of the inverse function. Discussions about the accuracy of the approximation will be included.
NASA Astrophysics Data System (ADS)
Yurdusev, Mehmet Ali; Firat, Mahmut
2009-02-01
SummaryIn this study, an adaptive neuro fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors including average monthly water bill, population, number of households, gross national product, monthly average temperature observed, monthly total rainfall, monthly average humidity observed and inflation rate. Water consumption modeling in this way will be more consistent than doing it using a single variable as more effective parameter could be incorporated. The ANFIS system is applied to modeling monthly water consumptions of Izmir, Turkey. The results indicated that ANFIS can be successfully applied for monthly water consumption modeling.
Measure of librarian pressure using fuzzy inference system: A case study in Longyan University
NASA Astrophysics Data System (ADS)
Huang, Jian-Jing
2014-10-01
As the hierarchy of middle managers in college's librarian. They may own much work pressure from their mind. How to adapt psychological problem, control the emotion and keep a good relationship in their work place, it becomes an important issue. Especially, they work in China mainland environment. How estimate the librarians work pressure and improve the quality of service in college libraries. Those are another serious issues. In this article, the authors would like discuss how can we use fuzzy inference to test librarian work pressure.
Heddam, Salim
2014-01-01
This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling. PMID:24057665
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
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. PMID:25607665
NASA Astrophysics Data System (ADS)
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
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
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
A grade-life fuzzy inference fusion prognostic model for aircraft engine bearings
NASA Astrophysics Data System (ADS)
Miao, Xuewen; Niu, Yongguo; Yang, Yun; Yin, Shuyue; Hong, Jie
2012-04-01
Prognostics and Health Management (PHM) technologies for potential application on aircraft have been maturing rapidly recently since it can ensure safety, equipment reliability, and reduction of costs. The service life prediction of aircraft engine is vital part of PHM technology. Research on practical and verifiable prediction methods for service life of bearing plays a critical role in improving the reliability and safety of aircraft engines. In the paper, the concept of Grade-Life (GL) is introduced to describe the service life of the bearing. A grade-life prognostic model of aircraft engine bearing, which is based on the fuzzy logic inference, is proposed. Firstly, the mathematical model is discussed, which is used to predict the physics-based GL (PGL). Then, the diagnostic estimation model based on SVM is given in details, which is exploited to predict the empirical GL (EPL). Thirdly, a fuzzy logic inference method is adopted to fuse two GL predicted results. Finally, the grade-life prognostic model is verified by the run-to-failure data acquired from accelerated life test of an aircraft bearing. The results accredit that this model provides for a more practical and reliable prediction for service life of bearings.
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.
NASA Technical Reports Server (NTRS)
Jani, Yashvant
1992-01-01
The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.
Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)
NASA Astrophysics Data System (ADS)
Kakar, Manish; Nyström, Håkan; Rye Aarup, Lasse; Jakobi Nøttrup, Trine; Rune Olsen, Dag
2005-10-01
The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.
Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.
Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L
2012-01-01
Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.
Ubeyli, Elif Derya
2009-03-01
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286
NASA Astrophysics Data System (ADS)
Ja'fari, Ahmad; Kadkhodaie-Ilkhchi, Ali; Sharghi, Yoosef; Ghanavati, Kiarash
2012-02-01
Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.
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
Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; 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
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. PMID:21349796
Adaptive neuro-fuzzy inference system for analysis of Doppler signals.
Ubeyli, Elif Derya
2006-01-01
In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The ophthalmic arterial Doppler signals were recorded from 128 subjects that 62 of them had suffered from ophthalmic artery stenosis and the rest of them had been healthy subjects. Some conclusions concerning the impacts of features on the detection of ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies (total classification accuracy was 97.59%) and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis. PMID:17945697
Mandal, S.K.; Agrawal, A.
1997-12-31
In this paper an attempt is made to forecast load using fuzzy neural network (FNN) for an integrated power system. Here, the proposed system uses a two stage FNN for a short term peak and average load forecasting (STPALF). The first stage FNN deals with the load forecasting and the second stage algorithm can be worked independently for network security. This technique is used to forecast load accurately on week days as well as holidays, weekends and some special occasions considering historical data of load and weather information and also take necessary control action for network security.
NASA Astrophysics Data System (ADS)
Kiso, Atsushi; Seki, Hirokazu
This paper describes a method for discriminating of the human forearm motions based on the myoelectric signals using an adaptive fuzzy inference system. In conventional studies, the neural network is often used to estimate motion intention by the myoelectric signals and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signals. This study designs the membership function and the fuzzy rules using the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. In addition, the characteristics of the myoelectric potential gradually change as a result of the muscle fatigue. Therefore, the motion discrimination should be performed by taking muscle fatigue into consideration. This study proposes a method to redesign the fuzzy inference system such that dynamic change of the myoelectric potential because of the muscle fatigue will be taken into account. Some experiments carried out using a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.
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.
NASA Technical Reports Server (NTRS)
Jani, Yashvant
1992-01-01
As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.
Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible.
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. PMID:26914521
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...
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.
Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu
2014-01-01
An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127
HAMEDIAN, Amir Abbas; JAVID, Allahbakhsh; MOTESADDI ZARANDI, Saeed; RASHIDI, Yousef; MAJLESI, Monireh
2016-01-01
Background: Since the industrial revolution, the rate of industrialization and urbanization has increased dramatically. Regarding this issue, specific regions mostly located in developing countries have been confronted with serious problems, particularly environmental problems among which air pollution is of high importance. Methods: Eleven parameters, including CO, SO2, PM10, PM2.5, O3, NO2, benzene, toluene, ethyl-benzene, xylene, and 1,3-butadiene, have been accounted over a period of two years (2011–2012) from five monitoring stations located at Tehran, Iran, were assessed by using fuzzy inference system and fuzzy c-mean clustering. Results: These tools showed that the quality of criteria pollutants between the year 2011 and 2012 did not as much effect the public health as the other pollutants did. Conclusion: Using the air EPA AQI, the quality of air, and also the managerial plans required to improve the quality can be misled. PMID:27516999
A Context-Aware Interactive Health Care System Based on Ontology and Fuzzy Inference.
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. PMID:26265236
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.
Singer, R.M.; Gross, K.C. ); Humenik, K.E. . Dept. of Computer Science)
1991-01-01
An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.
NASA Astrophysics Data System (ADS)
Zoveidavianpoor, Mansoor; Samsuri, Ariffin; Shadizadeh, Seyed Reza
2013-02-01
Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir.
Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.
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. PMID:26851475
Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Toro, Luis; Cavazos, Alberto; Colás, Rafael
2009-12-01
A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.
Human action recognition using meta-cognitive neuro-fuzzy inference system.
Subramanian, K; Suresh, S
2012-12-01
We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort. PMID:23186277
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.
Topuz, Emel; van Gestel, Cornelis A M
2016-01-01
The usage of Engineered Nanoparticles (ENPs) in consumer products is relatively new and there is a need to conduct environmental risk assessment (ERA) to evaluate their impacts on the environment. However, alternative approaches are required for ERA of ENPs because of the huge gap in data and knowledge compared to conventional pollutants and their unique properties that make it difficult to apply existing approaches. This study aims to propose an ERA approach for ENPs by integrating Analytical Hierarchy Process (AHP) and fuzzy inference models which provide a systematic evaluation of risk factors and reducing uncertainty about the data and information, respectively. Risk is assumed to be the combination of occurrence likelihood, exposure potential and toxic effects in the environment. A hierarchy was established to evaluate the sub factors of these components. Evaluation was made with fuzzy numbers to reduce uncertainty and incorporate the expert judgements. Overall score of each component was combined with fuzzy inference rules by using expert judgements. Proposed approach reports the risk class and its membership degree such as Minor (0.7). Therefore, results are precise and helpful to determine the risk management strategies. Moreover, priority weights calculated by comparing the risk factors based on their importance for the risk enable users to understand which factor is effective on the risk. Proposed approach was applied for Ag (two nanoparticles with different coating) and TiO2 nanoparticles for different case studies. Results verified the proposed benefits of the approach. PMID:27131016
Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson
2013-01-01
The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs. PMID:24109851
Error modeling of DEMs from topographic surveys of rivers using fuzzy inference systems
NASA Astrophysics Data System (ADS)
Bangen, Sara; Hensleigh, James; McHugh, Peter; Wheaton, Joseph
2016-02-01
Digital elevation models (DEMs) have become common place in the earth sciences as a tool to characterize surface topography and set modeling boundary conditions. All DEMs have a degree of inherent error that is propagated to subsequent models and analyses. While previous research has shown that DEM error is spatially variable it is often represented as spatially uniform for analytical simplicity. Fuzzy inference systems (FIS) offer a tractable approach for modeling spatially variable DEM error, including flexibility in the number of inputs and calibration of outputs based on survey technique and modeling environment. We compare three FIS error models for DEMs derived from TS surveys of wadeable streams and test them at 34 sites in the Columbia River basin. The models differ in complexity regarding the number/type of inputs and degree of site-specific parameterization. A 2-input FIS uses inputs derived from the topographic point cloud (slope, point density). A 4-input FIS adds interpolation error and 3-D point quality. The 5-input FIS adds bed-surface roughness estimates. Both the 4 and 5-input FIS model output were parameterized to site-specific values. In the wetted channel we found (i) the 5-input FIS resulted in lower mean δz due to including roughness, and (ii) the 4 and 5-input FIS resulted in a higher standard deviation and maximum δz due to the inclusion of site-specific bank heights. All three FIS gave plausible estimates of DEM error, with the two more complicated models offering an improvement in the ability to detect spatially localized areas of DEM uncertainty.
Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
NASA Astrophysics Data System (ADS)
Valyrakis, Manousos; Zhang, Hanqing
2014-05-01
Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health of river systems but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation.
NASA Astrophysics Data System (ADS)
Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.
2015-01-01
The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and
A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson's Disease.
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. PMID:26385550
Zhao, Shan; Huang, Gordon; Wang, Shuo; Wang, Xiuquan; Huang, Wendy
2016-07-28
A multi-level fuzzy-factorial inference approach was proposed to examine the sorption behavior of phenanthrene on palygorskite modified with a gemini surfactant. Fuzzy set theory was used to determine five experimentally controlled environmental factors with triangular membership functions, including initial concentration, added humid acid dose, ionic strength, temperature, and pH. The statistical significance of factors and their interactions affecting the sorption process was revealed through a multi-level factorial experiment. Initial concentration, ionic strength, and pH were identified as the most significant factors based on the multi-way ANOVA results. Examination of curvature effects of factors revealed the nonlinear complexity inherent in the sorption process. The potential interactions among experimental factors were detected, which is meaningful for providing a deep insight into the sorption mechanisms under the influences of factors at different levels. PMID:27163726
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
NASA Astrophysics Data System (ADS)
Mun, Johnathan; de Albuquerque, Nelson R.; Liong, Choong-Yeun; Salleh, Abdul Razak
2013-04-01
This paper presents the ASKE-Risk method, coupled with Fuzzy Inference Systems, and Monte Carlo Risk Simulation to measure and prioritize Individual Technical Competence of a value chain to assess changes in the human capital of a company. ASKE is an extension of the method Knowledge Value Added, which proposes the use of a proxy variable for measuring the flow of knowledge used in a key process, creating a relationship between the company's financial results and the resources used in each of the business processes.
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
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.
San, Phyo Phyo; Ling, Sai Ho; Nguyen, Hung T
2012-01-01
Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. PMID:23367375
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.
2015-01-01
Background The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Description Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/. Conclusions Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p
A hybrid conceptual-fuzzy inference streamflow modelling for the Letaba River system in South Africa
NASA Astrophysics Data System (ADS)
Katambara, Zacharia; Ndiritu, John G.
There has been considerable water resources developments in South Africa and other regions in the world in order to meet the ever-increasing water demands. These developments have not been matched with a similar development of hydrological monitoring systems and hence there is inadequate data for managing the developed water resources systems. The Letaba River system ( Fig. 1) is a typical case of such a system in South Africa. The available water on this river is over-allocated and reliable daily streamflow modelling of the Letaba River that adequately incorporates the main components and processes would be an invaluable aid to optimal operation of the system. This study describes the development of a calibrated hybrid conceptual-fuzzy-logic model and explores its capability in reproducing the natural processes and human effects on the daily stream flow in the Letaba River. The model performance is considered satisfactory in view of the complexity of the system and inadequacy of relevant data. Performance in modelling streamflow improves towards the downstream and matches that of a stand-alone fuzzy-logic model. The hybrid model obtains realistic estimates of the major system components and processes including the capacities of the farm dams and storage weirs and their trajectories. This suggests that for complex data-scarce River systems, hybrid conceptual-fuzzy-logic modelling may be used for more detailed and dependable operational and planning analysis than stand-alone fuzzy modelling. Further work will include developing and testing other hybrid model configurations.
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
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
NASA Astrophysics Data System (ADS)
Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad
2012-05-01
Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.
Image haze removal using a hybrid of fuzzy inference system and weighted estimation
NASA Astrophysics Data System (ADS)
Wang, Jyun-Guo; Tai, Shen-Chuan; Lin, Cheng-Jian
2015-05-01
The attenuation of the light transmitted through air can reduce image quality when taking a photograph outdoors, especially in a hazy environment. Hazy images often lack sufficient information for image recognition systems to operate effectively. In order to solve the aforementioned problems, this study proposes a hybrid method combining fuzzy theory with weighted estimation for the removal of haze from images. A transmission map is first created based on fuzzy theory. According to the transmission map, the proposed method automatically finds the possible atmospheric lights and refines the atmospheric lights by mixing these candidates. Weighted estimation is then employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Experimental results demonstrate the superiority of the proposed method over existing methods with regard to contrast, color depth, and the elimination of halo artifacts.
NASA Astrophysics Data System (ADS)
Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.
The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
NASA Astrophysics Data System (ADS)
Trianto, Andriantama Budi; Hadi, I. M.; Liong, The Houw; Purqon, Acep
2015-09-01
Indonesian economical development is growing well. It has effect for their invesment in Banks and the stock market. In this study, we perform prediction for the three blue chips of Indonesian bank i.e. BCA, BNI, and MANDIRI by using the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Takagi-Sugeno rules and Generalized bell (Gbell) as the membership function. Our results show that ANFIS perform good prediction with RMSE for BCA of 27, BNI of 5.29, and MANDIRI of 13.41, respectively. Furthermore, we develop an active strategy to gain more benefit. We compare between passive strategy versus active strategy. Our results shows that for the passive strategy gains 13 million rupiah, while for the active strategy gains 47 million rupiah in one year. The active investment strategy significantly shows gaining multiple benefit than the passive one.
Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang
2014-01-01
Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547
ERIC Educational Resources Information Center
Velastegui, Pamela J.
2013-01-01
This hypothesis-generating case study investigates the naturally emerging roles of technology brokers and technology leaders in three independent schools in New York involving 92 school educators. A multiple and mixed method design utilizing Social Network Analysis (SNA) and fuzzy set Qualitative Comparative Analysis (FSQCA) involved gathering…
Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail
2013-01-01
Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in
Poorbagher, Hadi; Moghaddam, Maryam Nasrollahpour; Eagderi, Soheil; Farahmand, Hamid
2016-07-01
The DNA breakage has been widely used in ecotoxicological studies to investigate effects of pesticides in fishes. The present study used a fuzzy inference system to quantify the breakage of DNA double strand in Aphanius sophiae exposed to the cypermethrin. The specimens were adapted to different temperatures and salinity for 14 days and then exposed to cypermethrin. DNA of each specimens were extracted, electrophoresed and photographed. A fuzzy system with three input variables and 27 rules were defined. The pixel value curve of DNA on each gel lane was obtained using ImageJ. The DNA breakage was quantified using the pixel value curve and fuzzy system. The defuzzified values were analyzed using a three-way analysis of variance. Cypermethrin had significant effects on DNA breakage. Fuzzy inference systems can be used as a tool to quantify the breakage of double strand DNA. DNA double strand of the gill of A. sophiae is sensitive enough to be used to detect cypermethrin in surface waters in concentrations much lower than those reported in previous studies. PMID:27000282
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.
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
1995-01-01
Particle Image Velocimetry provides a means of measuring the instantaneous 2-component velocity field across a planar region of a seeded flowfield. In this work only two camera, single exposure images are considered where both cameras have the same view of the illumination plane. Two competing techniques which yield unambiguous velocity vector direction information have been widely used for reducing the single exposure, multiple image data: cross-correlation and particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. The correlation technique requires identification of the correlation peak on the correlation plane corresponding to the average displacement of particles across the subregion. Noise on the images and particle dropout contribute to spurious peaks on the correlation plane, leading to misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work fuzzy logic techniques are used to determine the true correlation displacement peak even when it is not the maximum peak on the correlation plane, hence maximizing the information recovery from the correlation operation, maintaining the number of independent measurements and minimizing the number of spurious velocity vectors. Correlation peaks are correctly identified in both high and low seed density cases. The correlation velocity vector map can then be used as a guide for the particle tracking operation. Again fuzzy logic techniques are used, this time to identify the correct particle image pairings between exposures to determine particle displacements, and thus velocity. The advantage of this technique is the
Anbe, J; Tobi, T; Nakajima, H; Akasaka, T; Okinaga, K
1992-10-01
Since its establishment many researchers have been trying to automate the process of extracorporeal circulation (ECC). We developed a preliminary experimental model of an automatic regulatory system for ECC. The purpose of the system was to regulate basic hemodynamic parameters such as pump flow and withdrawal blood volume. It was divided into three main components: data sampling unit, central processing unit, and controlling unit. Based on this model we were able to achieve autoregulation of ECC using minimum configuration; however, the system lacked smoothness. This was partly because it was based on a "static" regulation system which used conditional statements having multiple parameters. In this study, we applied fuzzy logic to the former model to achieve more accurate and reliable regulation. We report experimental results for the new system and compare the data between clinical circulation in 13 infants (mean body weight, 13.32 +/- 5.99 kg) and experimental regulation in 7 mongrel dogs (mean body weight, 11.9 +/- 2.53 kg). The comparative study revealed no statistical difference between the two groups. This result suggests that the automatic regulation of ECC may be an alternative to manual operation by a professional perfusionist in the near future. PMID:10078307
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823
Amiri, Mohammad J; Abedi-Koupai, Jahangir; Eslamian, Sayed S; Mousavi, Sayed F; Hasheminejad, Hasti
2013-01-01
To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE. PMID:23383640
Jhin, Changho; Hwang, Keum Taek
2015-01-01
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167
Jhin, Changho; Hwang, Keum Taek
2015-01-01
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models. PMID:26474167
NASA Astrophysics Data System (ADS)
Teimouri, Reza; Sohrabpoor, Hamed
2013-12-01
Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.
NASA Technical Reports Server (NTRS)
Jani, Yashvant
1993-01-01
As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.
Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L
2013-02-01
The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies. PMID:23266912
NASA Astrophysics Data System (ADS)
Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi
2016-09-01
This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.
NASA Astrophysics Data System (ADS)
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2016-08-01
This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS
NASA Astrophysics Data System (ADS)
Memarian, Hadi; Pourreza Bilondi, Mohsen; Rezaei, Majid
2015-06-01
This work aims to assess the capability of co-active neuro-fuzzy inference system (CANFIS) for drought forecasting of Birjand, Iran through the combination of global climatic signals with rainfall and lagged values of Standardized Precipitation Index (SPI) index. Using stepwise regression and correlation analyses, the signals NINO 1 + 2, NINO 3, Multivariate Enso Index, Tropical Southern Atlantic index, Atlantic Multi-decadal Oscillation index, and NINO 3.4 were recognized as the effective signals on the drought event in Birjand. Based on the results from stepwise regression analysis and regarding the processor limitations, eight models were extracted for further processing by CANFIS. The metrics P-factor and D-factor were utilized for uncertainty analysis, based on the sequential uncertainty fitting algorithm. Sensitivity analysis showed that for all models, NINO indices and rainfall variable had the largest impact on network performance. In model 4 (as the model with the lowest error during training and testing processes), NINO 1 + 2(t-5) with an average sensitivity of 0.7 showed the highest impact on network performance. Next, the variables rainfall, NINO 1 + 2(t), and NINO 3(t-6) with the average sensitivity of 0.59, 0.28, and 0.28, respectively, could have the highest effect on network performance. The findings based on network performance metrics indicated that the global indices with a time lag represented a better correlation with El Niño Southern Oscillation (ENSO). Uncertainty analysis of the model 4 demonstrated that 68 % of the observed data were bracketed by the 95PPU and D-Factor value (0.79) was also within a reasonable range. Therefore, the fourth model with a combination of the input variables NINO 1 + 2 (with 5 months of lag and without any lag), monthly rainfall, and NINO 3 (with 6 months of lag) and correlation coefficient of 0.903 (between observed and simulated SPI) was selected as the most accurate model for drought forecasting using CANFIS
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.
Fuzzy Content-Based Retrieval in Image Databases.
ERIC Educational Resources Information Center
Wu, Jian Kang; Narasimhalu, A. Desai
1998-01-01
Proposes a fuzzy-image database model and a concept of fuzzy space; describes fuzzy-query processing in fuzzy space and fuzzy indexing on complete fuzzy vectors; and uses an example image database, the computer-aided facial-image inference and retrieval system (CAFIIR), for explanation throughout. (Author/LRW)
Aqil, M; Kita, I; Yano, A; Nishiyama, S
2006-01-01
It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other
NASA Astrophysics Data System (ADS)
Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.
2013-09-01
This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.
Torshabi, Ahmad Esmaili
2014-12-01
In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works. PMID:25412886
Swetapadma, Aleena; Yadav, Anamika
2015-01-01
Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance. PMID:26413088
NASA Astrophysics Data System (ADS)
Iphar, Melih; Yavuz, Mahmut; Ak, Hakan
2008-11-01
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.
NASA Astrophysics Data System (ADS)
Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo
2013-03-01
The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.
NASA Astrophysics Data System (ADS)
Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe
2013-12-01
The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.
Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise
2015-09-01
This paper presents a new model based on adaptive neuro-fuzzy inference systems (ANFIS) to predict oxygen consumption (V˙O2) from easily measured variables. The ANFIS prediction model consists of three ANFIS modules for estimating the Flex-HR parameters. Each module was developed based on clustering a training set of data samples relevant to that module and then the ANFIS prediction model was tested against a validation data set. Fifty-eight participants performed the Meyer and Flenghi step-test, during which heart rate (HR) and V˙O2 were measured. Results indicated no significant difference between observed and estimated Flex-HR parameters and between measured and estimated V˙O2 in the overall HR range, and separately in different HR ranges. The ANFIS prediction model (MAE = 3 ml kg(-1) min(-1)) demonstrated better performance than Rennie et al.'s (MAE = 7 ml kg(-1) min(-1)) and Keytel et al.'s (MAE = 6 ml kg(-1) min(-1)) models, and comparable performance with the standard Flex-HR method (MAE = 2.3 ml kg(-1) min(-1)) throughout the HR range. The ANFIS model thus provides practitioners with a practical, cost- and time-efficient method for V˙O2 estimation without the need for individual calibration. PMID:25959320
Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin
2014-06-01
A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis. PMID:24638918
NASA Astrophysics Data System (ADS)
Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika
2014-08-01
The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.
Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin
2014-01-01
Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. PMID:24566279
NASA Astrophysics Data System (ADS)
Ghanei, S.; Vafaeenezhad, H.; Kashefi, M.; Eivani, A. R.; Mazinani, M.
2015-04-01
Tracing microstructural evolution has a significant importance and priority in manufacturing lines of dual-phase steels. In this paper, an artificial intelligence method is presented for on-line microstructural characterization of dual-phase steels. A new method for microstructure characterization based on the theory of magnetic Barkhausen noise nondestructive testing method is introduced using adaptive neuro-fuzzy inference system (ANFIS). In order to predict the accurate martensite volume fraction of dual-phase steels while eliminating the effect and interference of frequency on the magnetic Barkhausen noise outputs, the magnetic responses were fed into the ANFIS structure in terms of position, height and width of the Barkhausen profiles. The results showed that ANFIS approach has the potential to detect and characterize microstructural evolution while the considerable effect of the frequency on magnetic outputs is overlooked. In fact implementing multiple outputs simultaneously enables ANFIS to approach to the accurate results using only height, position and width of the magnetic Barkhausen noise peaks without knowing the value of the used frequency.
NASA Astrophysics Data System (ADS)
Ghaedi, M.; Hosaininia, R.; Ghaedi, A. M.; Vafaei, A.; Taghizadeh, F.
2014-10-01
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope (SEM), Brunauer-Emmett-Teller (BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55 m2/g) and low pore size (<22.46 Å) and average particle size lower than 48.8 Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30 min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R2) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.
Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F
2014-10-15
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. PMID:24858196
NASA Astrophysics Data System (ADS)
Kentel, E.; Dogulu, N.
2015-12-01
In Turkey the experience and data required for a hydrological model setup is limited and very often not available. Moreover there are many ungauged catchments where there are also many planned projects aimed at utilization of water resources including development of existing hydropower potential. This situation makes runoff prediction at locations with lack of data and ungauged locations where small hydropower plants, reservoirs, etc. are planned an increasingly significant challenge and concern in the country. Flow duration curves have many practical applications in hydrology and integrated water resources management. Estimation of flood duration curve (FDC) at ungauged locations is essential, particularly for hydropower feasibility studies and selection of the installed capacities. In this study, we test and compare the performances of two methods for estimating FDCs in the Western Black Sea catchment, Turkey: (i) FDC based on Map Correlation Method (MCM) flow estimates. MCM is a recently proposed method (Archfield and Vogel, 2010) which uses geospatial information to estimate flow. Flow measurements of stream gauging stations nearby the ungauged location are the only data requirement for this method. This fact makes MCM very attractive for flow estimation in Turkey, (ii) Adaptive Neuro-Fuzzy Inference System (ANFIS) is a data-driven method which is used to relate FDC to a number of variables representing catchment and climate characteristics. However, it`s ease of implementation makes it very useful for practical purposes. Both methods use easily collectable data and are computationally efficient. Comparison of the results is realized based on two different measures: the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE) value. Ref: Archfield, S. A., and R. M. Vogel (2010), Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, doi:10.1029/2009WR008481.
NASA Astrophysics Data System (ADS)
Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.
2015-12-01
This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly.
Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests
NASA Technical Reports Server (NTRS)
Douglas, Freddie; Bourgeois, Edit Kaminsky
2005-01-01
The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).
A spatial neural fuzzy network for estimating pan evaporation at ungauged sites
NASA Astrophysics Data System (ADS)
Chung, C.-H.; Chiang, Y.-M.; Chang, F.-J.
2011-11-01
Evaporation is an essential reference to the management of water resources. In this study, a hybrid model that integrates a spatial neural fuzzy network with the kringing method is developed to estimate pan evaporation at ungauged sites. The adaptive network-based fuzzy inference system (ANFIS) can extract the nonlinear relationship of observations, while kriging is an excellent geostatistical interpolator. Three-year daily data collected from nineteen meteorological stations covering the whole of Taiwan are used to train and test the constructed model. The pan evaporation (Epan) at ungauged sites can be obtained through summing up the outputs of the spatially weighted ANFIS and the residuals adjusted by kriging. Results indicate that the proposed AK model (hybriding ANFIS and kriging) can effectively improve the accuracy of Epan estimation as compared with that of empirical formula. This hybrid model demonstrates its reliability in estimating the spatial distribution of Epan and consequently provides precise Epan estimation by taking geographical features into consideration.
A spatial neural fuzzy network for estimating pan evaporation at ungauged sites
NASA Astrophysics Data System (ADS)
Chung, C.-H.; Chiang, Y.-M.; Chang, F.-J.
2012-01-01
Evaporation is an essential reference to the management of water resources. In this study, a hybrid model that integrates a spatial neural fuzzy network with the kringing method is developed to estimate pan evaporation at ungauged sites. The adaptive network-based fuzzy inference system (ANFIS) can extract the nonlinear relationship of observations, while kriging is an excellent geostatistical interpolator. Three-year daily data collected from nineteen meteorological stations covering the whole of Taiwan are used to train and test the constructed model. The pan evaporation (Epan) at ungauged sites can be obtained through summing up the outputs of the spatially weighted ANFIS and the residuals adjusted by kriging. Results indicate that the proposed AK model (hybriding ANFIS and kriging) can effectively improve the accuracy of Epan estimation as compared with that of empirical formula. This hybrid model demonstrates its reliability in estimating the spatial distribution of Epan and consequently provides precise Epan estimation by taking geographical features into consideration.
Fuzzy logic in control systems: Fuzzy logic controller. I, II
NASA Technical Reports Server (NTRS)
Lee, Chuen Chien
1990-01-01
Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.
Use of fuzzy logic in lignite inventory estimation
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.
Fuzzy systems in high-energy physics
NASA Astrophysics Data System (ADS)
Castellano, Marcello; Masulli, Francesco; Penna, Massimo
1996-06-01
exploiting the linguistic knowledge available (structure identification problem) and by using the information contained in a data set (parameter estimation problem). The fuzzy system has been found to be effective for the classification tasks of about 2 by 10-3 hadron contamination at 90% of electron acceptance. A comparison between the adaptive system results and the others previous ones obtained by using both statistical and neural network based methodologies also is presented.
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.
Modeling Research Project Risks with Fuzzy Maps
ERIC Educational Resources Information Center
Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana
2009-01-01
The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…
Software Packages to Deal with Fuzzy Systems
NASA Astrophysics Data System (ADS)
Zahariev, Z.
2007-10-01
This paper investigates currently available software packages dealing with fuzzy inference systems (FIS). Fifteen packages are investigated and are described here. Some comparisons are created. At the end there are some conclusions.
NASA Astrophysics Data System (ADS)
Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad
2014-09-01
The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.
NASA Astrophysics Data System (ADS)
Khoshbin, Fatemeh; Bonakdari, Hossein; Hamed Ashraf Talesh, Seyed; Ebtehaj, Isa; Zaji, Amir Hossein; Azimi, Hamed
2016-06-01
In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron-artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.
Network-Based Management Procedures.
ERIC Educational Resources Information Center
Buckner, Allen L.
Network-based management procedures serve as valuable aids in organizational management, achievement of objectives, problem solving, and decisionmaking. Network techniques especially applicable to educational management systems are the program evaluation and review technique (PERT) and the critical path method (CPM). Other network charting…
DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.
Singh, A; Quek, C; Cho, S Y
2008-04-01
superior performance. Extensive experiments have been conducted to test the effectiveness of these two networks, using various clustering algorithms. It follows that the SDCT and UDCT clustering algorithms are particularly suited to networks based on the Yager inference rule. PMID:18390309
Type-II Fuzzy Decision Support System for Fertilizer
Ashraf, Ather; Sarwar, Mansoor
2014-01-01
Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, whether to go for a triangle membership function or a trapezoidal membership function. In this paper we use a type-II fuzzy set to overcome this uncertainty, and develop a fuzzy decision support system of fertilizers based on a type-II fuzzy set. This type-II fuzzy system takes cropping time and soil nutrients in the form of spatial surfaces as input, fuzzifies it using a type-II fuzzy membership function, and implies fuzzy rules on it in the fuzzy inference engine. The output of the fuzzy inference engine, which is in the form of interval value type-II fuzzy sets, reduced to an interval type-I fuzzy set, defuzzifies it to a crisp value and generates a spatial surface of fertilizers. This spatial surface shows the spatial trend of the required amount of fertilizer needed to cultivate a specific crop. The complexity of our algorithm is O(mnr), where m is the height of the raster, n is the width of the raster, and r is the number of expert rules. PMID:24892071
Comparison between the performance of two classes of fuzzy controllers
NASA Technical Reports Server (NTRS)
Janabi, T. H.; Sultan, L. H.
1992-01-01
This paper presents an application comparison between two classes of fuzzy controllers: the Clearness Transformation Fuzzy Controller (CTFC) and the CRI-based Fuzzy Controller. The comparison is performed by studying the application of the controllers to simulation examples of nonlinear systems. The CTFC is a new approach for the organization of fuzzy controllers based on a cognitive model of parameter driven control, the notion of fuzzy patterns to represent fuzzy knowledge and the Clearness Transformation Rule of Inference (CTRI) for approximate reasoning. The approach facilitates the implementation of the basic modules of the controller: the fuzzifier, defuzzifier, and the control protocol in a rule-based architecture. The CTRI scheme for approximate reasoning does not require the formation of fuzzy relation matrices yielding improved performance in comparison with the traditional organization of fuzzy controllers.
NASA Astrophysics Data System (ADS)
Juels, Ari
The purpose of this chapter is to introduce fuzzy commitment, one of the earliest and simplest constructions geared toward cryptography over noisy data. The chapter also explores applications of fuzzy commitment to two problems in data security: (1) secure management of biometrics, with a focus on iriscodes, and (2) use of knowledge-based authentication (i.e., personal questions) for password recovery.
NASA Astrophysics Data System (ADS)
Subashini, L.; Vasudevan, M.
2012-02-01
Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.
NASA Astrophysics Data System (ADS)
Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.
2016-02-01
All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.
Fault classification by neural networks and fuzzy logic
Chwan-Hwa ``John`` Wu; Chihwen Li; Shih, H.; Alexion, C.C.; Ovick, N.L.; Murphy, J.H.
1995-01-25
A neural fuzzy-based and a backpropagation neural network-based fault classifier for a three-phase motor will be described in this paper. In order to acquire knowledge, the neural fuzzy classifier incorporates a learning technique to automatically generate membership functions for fuzzy rules, and the backpropagation algorithm is used to train the neural network model. Therefore, in this paper, the preprocessing of signals, fuzzy and neural models, training methods, implementations for real-time response and testing results will be discussed in detail. Furthermore, the generalization capabilities of the neural fuzzy- and backpropagation-based classifiers for waveforms with varying magnitudes, frequencies, noises and positions of spikes and chops in a cycle of a sine wave will be investigated, and the computation requirements needed to achieve real-time response for both fuzzy and neural methods will be compared. {copyright} 1995 {ital American} {ital Institute} {ital of} {ital Physics}
Network-based modular latent structure analysis
2014-01-01
Background High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership. Methods Here we describe a method based on community detection in the co-expression network. It consists of inference-based network construction, module detection, and interacting latent factor detection from modules. Results In simulations, the method outperformed projection-based modular latent factor discovery when the input signals were not Gaussian. We also demonstrate the method's value in real data analysis. Conclusions The new method nMLSA (network-based modular latent structure analysis) is effective in detecting latent structures, and is easy to extend to non-linear cases. The method is available as R code at http://web1.sph.emory.edu/users/tyu8/nMLSA/. PMID:25435002
NASA Astrophysics Data System (ADS)
Dalkilic, Turkan Erbay; Apaydin, Aysen
2009-11-01
In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.
Image Edge Extraction via Fuzzy Reasoning
NASA Technical Reports Server (NTRS)
Dominquez, Jesus A. (Inventor); Klinko, Steve (Inventor)
2008-01-01
A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.
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.
Image segmentation using trainable fuzzy set classifiers
NASA Astrophysics Data System (ADS)
Schalkoff, Robert J.; Carver, Albrecht E.; Gurbuz, Sabri
1999-07-01
A general image analysis and segmentation method using fuzzy set classification and learning is described. The method uses a learned fuzzy representation of pixel region characteristics, based upon the conjunction and disjunction of extracted and derived fuzzy color and texture features. Both positive and negative exemplars of some visually apparent characteristic which forms the basis of the inspection, input by a human operator, are used together with a clustering algorithm to construct positive similarity membership functions and negative similarity membership functions. Using these composite fuzzified images, P and N, are produced using fuzzy union. Classification is accomplished via image defuzzification, whereby linguistic meaning is assigned to each pixel in the fuzzy set using a fuzzy inference operation. The technique permits: (1) strict color and texture discrimination, (2) machine learning of color and texture characteristics of regions, (3) and judicious labeling of each pixel based upon leaned fuzzy representation and fuzzy classification. This approach appears ideal for applications involving visual inspection and allows the development of image-based inspection systems which may be trained and used by relatively unskilled workers. We show three different examples involving the visual inspection of mixed waste drums, lumber and woven fabric.
Fuzzy logic and neural networks
Loos, J.R.
1994-11-01
Combine fuzzy logic`s fuzzy sets, fuzzy operators, fuzzy inference, and fuzzy rules - like defuzzification - with neural networks and you can arrive at very unfuzzy real-time control. Fuzzy logic, cursed with a very whimsical title, simply means multivalued logic, which includes not only the conventional two-valued (true/false) crisp logic, but also the logic of three or more values. This means one can assign logic values of true, false, and somewhere in between. This is where fuzziness comes in. Multi-valued logic avoids the black-and-white, all-or-nothing assignment of true or false to an assertion. Instead, it permits the assignment of shades of gray. When assigning a value of true or false to an assertion, the numbers typically used are {open_quotes}1{close_quotes} or {open_quotes}0{close_quotes}. This is the case for programmed systems. If {open_quotes}0{close_quotes} means {open_quotes}false{close_quotes} and {open_quotes}1{close_quotes} means {open_quotes}true,{close_quotes} then {open_quotes}shades of gray{close_quotes} are any numbers between 0 and 1. Therefore, {open_quotes}nearly true{close_quotes} may be represented by 0.8 or 0.9, {open_quotes}nearly false{close_quotes} may be represented by 0.1 or 0.2, and {close_quotes}your guess is as good as mine{close_quotes} may be represented by 0.5. The flexibility available to one is limitless. One can associate any meaning, such as {open_quotes}nearly true{close_quotes}, to any value of any granularity, such as 0.9999. 2 figs.
NASA Astrophysics Data System (ADS)
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.
Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad
2016-06-01
Here, 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 taggingmore » 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.« less
Modelling of Reservoir Operations using Fuzzy Logic and ANNs
NASA Astrophysics Data System (ADS)
Van De Giesen, N.; Coerver, B.; Rutten, M.
2015-12-01
Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, <0,1>, was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.
NASA Astrophysics Data System (ADS)
King, Gary; Rosen, Ori; Tanner, Martin A.
2004-09-01
This collection of essays brings together a diverse group of scholars to survey the latest strategies for solving ecological inference problems in various fields. The last half-decade has witnessed an explosion of research in ecological inference--the process of trying to infer individual behavior from aggregate data. Although uncertainties and information lost in aggregation make ecological inference one of the most problematic types of research to rely on, these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, by business in marketing research, and by governments in policy analysis.
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.
Fuzzy logic controllers: From development to deployment
Bonissone, P.P.; Chiang, K.H.
1994-12-31
We view fuzzy logic control technology as a high level language in which we can efficiently define and synthesize non-linear controllers for a given process. We contrast fuzzy Proportional Integral (PI) controllers with conventional PI and two dimensional sliding mode controllers. Then we compare the development of Fuzzy Logic Controllers (FLC) with that of Knowledge Based System (KBS) applications. We decompose the comparison into reasoning tasks (representation, inference, and control) and application tasks (acquisition, development, validation, compilation, and deployment). After reviewing the reasoning tasks, we focus on the compilation of fuzzy rule bases into fast access lookup tables. These tables can be used by a simplified run-time engine to determine the TLC`s crisp output for a given input.
Hybrid fuzzy regression with trapezoidal fuzzy data
NASA Astrophysics Data System (ADS)
Razzaghnia, T.; Danesh, S.; Maleki, A.
2011-12-01
In this regard, this research deals with a method for hybrid fuzzy least-squares regression. The extension of symmetric triangular fuzzy coefficients to asymmetric trapezoidal fuzzy coefficients is considered as an effective measure for removing unnecessary fuzziness of the linear fuzzy model. First, trapezoidal fuzzy variable is applied to derive a bivariate regression model. In the following, normal equations are formulated to solve the four parts of hybrid regression coefficients. Also the model is extended to multiple regression analysis. Eventually, method is compared with Y-H.O. chang's model.
Engineering application based on fuzzy approach
NASA Astrophysics Data System (ADS)
Pislaru, Marius; Avasilcai, Silvia; Trandabat, Alexandru
2011-12-01
The article focus on an application of chemical engineering. A fuzzy modeling methodology designed to determinate two relevant characteristics of a chemical compound (ferrocenylsiloxane polyamide) for self-assembling - surface tension and maximum UV absorbance measured as temperature and concentration functions. One of the most important parts of a fuzzy rule-based inference system for the polyamide solution characteristics determinations is that it allows to interpret the knowledge contained in the model and also to improve it with a-priori knowledge. The results obtained through proposed method are highly accurate and its can be optimized by utilizing the available information during the modeling process. The results showed that it is feasible in theory and reliable on calculation applying Mamdani fuzzy inference system to the estimation of optical and surface properties of a polyamide solution.
Engineering application based on fuzzy approach
NASA Astrophysics Data System (ADS)
Pislaru, Marius; Avasilcai, Silvia; Trandabat, Alexandru
2012-01-01
The article focus on an application of chemical engineering. A fuzzy modeling methodology designed to determinate two relevant characteristics of a chemical compound (ferrocenylsiloxane polyamide) for self-assembling - surface tension and maximum UV absorbance measured as temperature and concentration functions. One of the most important parts of a fuzzy rule-based inference system for the polyamide solution characteristics determinations is that it allows to interpret the knowledge contained in the model and also to improve it with a-priori knowledge. The results obtained through proposed method are highly accurate and its can be optimized by utilizing the available information during the modeling process. The results showed that it is feasible in theory and reliable on calculation applying Mamdani fuzzy inference system to the estimation of optical and surface properties of a polyamide solution.
Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Ma, Yongwen; Wang, Yan
2013-12-01
An on-line hybrid fuzzy-neural soft-sensing model-based control system was developed to optimize dissolved oxygen concentration in a bench-scale anaerobic/anoxic/oxic (A(2)/O) process. In order to improve the performance of the control system, a self-adapted fuzzy c-means clustering algorithm and adaptive network-based fuzzy inference system (ANFIS) models were employed. The proposed control system permits the on-line implementation of every operating strategy of the experimental system. A set of experiments involving variable hydraulic retention time (HRT), influent pH (pH), dissolved oxygen in the aerobic reactor (DO), and mixed-liquid return ratio (r) was carried out. Using the proposed system, the amount of COD in the effluent stabilized at the set-point and below. The improvement was achieved with optimum dissolved oxygen concentration because the performance of the treatment process was optimized using operating rules implemented in real time. The system allows various expert operational approaches to be deployed with the goal of minimizing organic substances in the outlet while using the minimum amount of energy. PMID:24052227
A transductive neuro-fuzzy controller: application to a drilling process.
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. PMID:20659865
NASA Astrophysics Data System (ADS)
Volosencu, Constantin; Curiac, Daniel-Ioan
2013-12-01
This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods - linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system - to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.
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.
Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers
NASA Technical Reports Server (NTRS)
Yuan, Bo; Klir, George J.
1997-01-01
In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.
NASA Astrophysics Data System (ADS)
Caticha, Ariel
2011-03-01
In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.
Dynamic Network-Based Epistasis Analysis: Boolean Examples
Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.
2011-01-01
In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and
Prediction of conductivity by adaptive neuro-fuzzy model.
Akbarzadeh, S; Arof, A K; Ramesh, S; Khanmirzaei, M H; Nor, R M
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
Prediction of Conductivity by Adaptive Neuro-Fuzzy Model
Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.
2014-01-01
Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582
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.
Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach
NASA Astrophysics Data System (ADS)
Taufik, Afirah; Sakinah Syed Ahmad, Sharifah
2016-06-01
The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.
Dissecting a Network-Based Education System
ERIC Educational Resources Information Center
Davis, Tiffany; Yoo, Seong-Moo; Pan, Wendi
2005-01-01
The Alabama Learning Exchange (ALEX; www.alex.state.al.us) is a network-based education system designed and implemented to help improve education in Alabama. It accomplishes this goal by providing a single location for the state's K-12 educators to find information that will help improve their classroom effectiveness. The ALEX system includes…
Network based high performance concurrent computing
Sunderam, V.S.
1991-01-01
The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.
Elements of Network-Based Assessment
ERIC Educational Resources Information Center
Gibson, David
2007-01-01
Elements of network-based assessment systems are envisioned based on recent advances in knowledge and practice in learning theory, assessment design and delivery, and semantic web interoperability. The architecture takes advantage of the meditating role of technology as well as recent models of assessment systems. This overview of the elements…
Network-Based Classrooms: Promises and Realities.
ERIC Educational Resources Information Center
Bruce, Bertram C., Ed.; And Others
Exploring how new technologies and new pedagogies transform and are transformed by existing institutions, this book presents 14 essays that discuss network-based classrooms in which students use communications software on computer networks to converse in writing. The first part of the book discusses general themes and issues of the ENFI…
Neural network based architectures for aerospace applications
NASA Technical Reports Server (NTRS)
Ricart, Richard
1987-01-01
A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.
Aggelopoulos, Nikolaos C
2015-08-01
Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. PMID:25976632
Fuzzy logic controller optimization
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.
NASA Technical Reports Server (NTRS)
Abihana, Osama A.; Gonzalez, Oscar R.
1993-01-01
The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.
Performance of Geno-Fuzzy Model on rainfall-runoff predictions in claypan watersheds
Technology Transfer Automated Retrieval System (TEKTRAN)
Fuzzy logic provides a relatively simple approach to simulate complex hydrological systems while accounting for the uncertainty of environmental variables. The objective of this study was to develop a fuzzy inference system (FIS) with genetic algorithm (GA) optimization for membership functions (MF...
Kacewicz, M.
1987-11-01
An approach for the description of uncertainty in geology using fuzzy-set theory and an example of slope stability problem is presented. Soil parameters may be described by fuzzy sets. The fuzzy method of slope stability estimation is considered and verified in the case of one of Warsaw's (Poland) slopes.
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.
Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
De Maeyer, Dries; Weytjens, Bram; De Raedt, Luc; Marchal, Kathleen
2016-01-01
In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html PMID:26802430
Decentralized fuzzy control of multiple nonholonomic vehicles
Driessen, B.J.; Feddema, J.T.; Kwok, K.S.
1997-09-01
This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.
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.
Zargham, M.R.
1995-06-01
Recently, fuzzy logic has been applied to many areas, such as process control, image understanding, robots, expert systems, and decision support systems. This paper will explain the basic concepts of fuzzy logic and its application in different fields. The steps to design a control system will be explained in detail. Fuzzy control is the first successful industrial application of fuzzy logic. A fuzzy controller is able to control systems which previously could only be controlled by skilled operators. In recent years Japan has achieved significant progress in this area and has applied it to variety of products such as cruise control for cars, video cameras, rice cookers, washing machines, etc.
Self-organizing neural network as a fuzzy classifier
Mitra, S.; Pal, S.K.
1994-03-01
This paper describes a self-organizing artificial neural network, based on Kohonen`s model of self-organization, which is capable of handling fuzzy input and of providing fuzzy classification. Unlike conventional neural net models, this algorithm incorporates fuzzy set-theoretic concepts at various stages. The input vector consists of membership values for linguistic properties along with some contextual class membership information which is used during self-organization to permit efficient modeling of fuzzy (ambiguous) patterns. A new definition of gain factor for weight updating is proposed. An index of disorder involving mean square distance between the input and weight vectors is used to determine a measure of the ordering of the output space. This controls the number of sweeps required in the process. Incorporation of the concept of fuzzy partitioning allows natural self-organization of the input data, especially when they have ill-defined boundaries. The output of unknown test patterns is generated in terms of class membership values. Incorporation of fuzziness in input and output is seen to provide better performance as compared to the original Kohonen model and the hard version. The effectiveness of this algorithm is demonstrated on the speech recognition problem for various network array sizes, training sets and gain factors. 24 refs.
Fuzzy Sets and Mathematical Education.
ERIC Educational Resources Information Center
Alsina, C.; Trillas, E.
1991-01-01
Presents the concept of "Fuzzy Sets" and gives some ideas for its potential interest in mathematics education. Defines what a Fuzzy Set is, describes why we need to teach fuzziness, gives some examples of fuzzy questions, and offers some examples of activities related to fuzzy sets. (MDH)
NASA Astrophysics Data System (ADS)
Bazzazi, Abbas Aghajani; Esmaeili, Mohammad
2012-12-01
Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (
Optimal halftoning for network-based imaging
NASA Astrophysics Data System (ADS)
Ostromoukhov, Victor
2000-12-01
In this contribution, we introduce a multiple depth progressive representation for network-based still and moving images. A simple quantization algorithm associated with this representation provides optimal image quality. By optimum, we mean the best possible visual quality for a given value of information under real life constraints such as physical, psychological , or legal constraints. A special variant of the algorithm, multi-depth coherent error diffusion, addresses a specific problem of temporal coherence between frames in moving images. The output produced with our algorithm is visually pleasant because its Fourier spectrum is close to the 'blue noise'.
A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Zeinali, Mohammadjavad; Amri Mazlan, Saiful; Yasser Abd Fatah, Abdul; Zamzuri, Hairi
2013-12-01
A magnetorheological (MR) damper is a promising appliance for semi-active suspension systems, due to its capability of damping undesired movement using an adequate control strategy. This research has been carried out a phenomenological dynamic model of two MR dampers using an adaptive-network-based fuzzy inference system (ANFIS) approach. Two kinds of Lord Corporation MR damper (a long stroke damper and a short stroke damper) were used in experiments, and then modeled using the experimental results. In addition, an investigation of the influence of the membership function selection on predicting the behavior of the MR damper and obtaining a mathematical model was conducted to realize the relationship between input current, displacement, and velocity as the inputs and force as output. The results demonstrate that the proposed models for both short stroke and long stroke MR dampers have successfully predicted the behavior of the MR damper with adequate accuracy, and an equation is presented to precisely describe the behavior of each MR damper.
A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System
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
A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System.
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. PMID:27482707
Network-Based Protein Biomarker Discovery Platforms
Kim, Minhyung
2016-01-01
The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885
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.
Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic
NASA Technical Reports Server (NTRS)
Yang, Li-Farn
1996-01-01
This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.
Fuzzy mathematical techniques with applications
Kandel, A.
1986-01-01
This text presents the basic concepts of fuzzy set theory within a context of real-world applications. The book is self-contained and can be used as a starting point for people interested in this fast growing field as well as by researchers looking for new application techniques. The section on applications includes: Manipulation of knowledge in expert systems; relational database structures; pattern clustering; analysis of transient behavior in digital systems; modeling of uncertainty and search trees. Contents: Fuzzy sets; Possibility theory and fuzzy quantification; Fuzzy functions; Fuzzy events and fuzzy statistics; Fuzzy relations; Fuzzy logics; Some applications; Bibliography.
FEM Optimization of Spin Forming Using a Fuzzy Control Algorithm
NASA Astrophysics Data System (ADS)
Yoshihara, S.; Ray, P.; MacDonald, B. J.; Koyama, H.; Kawahara, M.
2004-06-01
Finite element (FE) simulation of the manufacturing of a conical nosing such as a pressure vessel from circular tubes, using the spin forming method, was performed on the commercially available software package, ANSYS/LS-DYNA3D. The finite element method (FEM) provides a powerful tool for evaluating the potential to form the pressure vessel with proposed modifications to the process. The use of fuzzy logic inference as a control system to achieve the designed shape of the pressure vessel was investigated using the FEM. The path of the roller as a process parameter was decided by the fuzzy inference control algorithm from information of the result of deformation of each element respectively. The fuzzy control algorithm investigated was validated from the results of the production process time and the deformed shape using FE simulation.
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.
Research of Expended Production Rule Based on Fuzzy Conceptual Graphs*
NASA Astrophysics Data System (ADS)
Liu, Peiqi; Li, Longji; Zhang, Linye; Li, Zengzhi
In the knowledge engineering, the fuzzy conceptual graphs and the production rule are two important knowledge representation methods. Because the confidence information can't be represented in the fuzzy conceptual graphs and the fuzzy knowledge can't be represented in the production rules, the ability of their knowledge representation is grievous insufficiency. In this paper, the extended production rule which is a new knowledge representation method has been presented. In the extended production rule, the antecedent and consequent of a rule are represented by fuzzy conceptual graphs, and the sustaining relation between antecedent and consequent is the confidence. The rule combines the fuzzy knowledge with the confidence effectually. It not only retains the semantic plentifulness of facts and proposition, but also makes the reasoning results more effectively. According to the extended production rule, the uncertain reasoning algorithm based on fuzzy conceptual graphs is designed. By the experiment test and analysis, the reasoning effects of the extended production rule are more in reason. The researching results are applied in the designed of uncertain inference engine in fuzzy expert system.
MEDUSA: a fuzzy expert system for medical diagnosis of acute abdominal pain.
Fathi-Torbaghan, M; Meyer, D
1994-12-01
Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rule-based, heuristic and case-based reasoning on the basis of imprecise information. The central idea of the integration is to use case-based reasoning for the management of special cases, and rule-based reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations. PMID:7869951
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.
Network-based recommendation algorithms: A review
NASA Astrophysics Data System (ADS)
Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš
2016-06-01
Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.
A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter
NASA Technical Reports Server (NTRS)
Krasowski, M. J.; Dickens, D. E.
1992-01-01
A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.
Some Properties of Fuzzy Soft Proximity Spaces
Demir, İzzettin; Özbakır, Oya Bedre
2015-01-01
We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities. PMID:25793224
Fuzzy automata and pattern matching
NASA Technical Reports Server (NTRS)
Setzer, C. B.; Warsi, N. A.
1986-01-01
A wide-ranging search for articles and books concerned with fuzzy automata and syntactic pattern recognition is presented. A number of survey articles on image processing and feature detection were included. Hough's algorithm is presented to illustrate the way in which knowledge about an image can be used to interpret the details of the image. It was found that in hand generated pictures, the algorithm worked well on following the straight lines, but had great difficulty turning corners. An algorithm was developed which produces a minimal finite automaton recognizing a given finite set of strings. One difficulty of the construction is that, in some cases, this minimal automaton is not unique for a given set of strings and a given maximum length. This algorithm compares favorably with other inference algorithms. More importantly, the algorithm produces an automaton with a rigorously described relationship to the original set of strings that does not depend on the algorithm itself.
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters
Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas
2013-01-01
We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization. PMID:24396342
Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes.
Ahvar, Ehsan; Lee, Gyu Myoung; Han, Son N; Crespi, Noel; Khan, Imran
2016-01-01
User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users' privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation. PMID:27355951
Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes
Ahvar, Ehsan; Lee, Gyu Myoung; Han, Son N.; Crespi, Noel; Khan, Imran
2016-01-01
User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users’ privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation. PMID:27355951
Simplify fuzzy control implementation
Stoll, K.E.; Ralston, P.A.S.; Ramaganesan, S. )
1993-07-01
A controller that uses fuzzy rules provides better response than a conventional linear controller in some applications. The rules are best implemented as a breakpoint function. A level control example illustrates the technique and advantages over proportional-integral (PI) control. In numerous papers on fuzzy controller development, emphasis has been primarily on formal inferencing, membership functions, and steps in building a fuzzy relation, as described by Zadeh. The rationale used in formulating the required set of rules is usually neglected, and the interpretation of the final controller as an input-output algorithm is overlooked. Also, the details of fuzzy mathematics are unfamiliar to many engineers and the implementation appears cumbersome to most. Process description and control instrumentation. This article compares a fuzzy controller designed by specifying a breakpoint function with a traditional PI controller for a level control system on a laboratory scale. In this discussion, only setpoint changes are considered.
Hydrograph estimation with fuzzy chain model
NASA Astrophysics Data System (ADS)
Güçlü, Yavuz Selim; Şen, Zekai
2016-07-01
Hydrograph peak discharge estimation is gaining more significance with unprecedented urbanization developments. Most of the existing models do not yield reliable peak discharge estimations for small basins although they provide acceptable results for medium and large ones. In this study, fuzzy chain model (FCM) is suggested by considering the necessary adjustments based on some measurements over a small basin, Ayamama basin, within Istanbul City, Turkey. FCM is based on Mamdani and the Adaptive Neuro Fuzzy Inference Systems (ANFIS) methodologies, which yield peak discharge estimation. The suggested model is compared with two well-known approaches, namely, Soil Conservation Service (SCS)-Snyder and SCS-Clark methodologies. In all the methods, the hydrographs are obtained through the use of dimensionless unit hydrograph concept. After the necessary modeling, computation, verification and adaptation stages comparatively better hydrographs are obtained by FCM. The mean square error for the FCM is many folds smaller than the other methodologies, which proves outperformance of the suggested methodology.
Automation of Network-Based Scientific Workflows
Altintas, I.; Barreto, R.; Blondin, J. M.; Cheng, Z.; Critchlow, T.; Khan, A.; Klasky, Scott A; Ligon, J.; Ludaescher, B.; Mouallem, P. A.; Parker, S.; Podhorszki, Norbert; Shoshani, A.; Silva, C.; Vouk, M. A.
2007-01-01
Comprehensive, end-to-end, data and workflow management solutions are needed to handle the increasing complexity of processes and data volumes associated with modern distributed scientific problem solving, such as ultra-scale simulations and high-throughput experiments. The key to the solution is an integrated network-based framework that is functional, dependable, fault-tolerant, and supports data and process provenance. Such a framework needs to make development and use of application workflows dramatically easier so that scientists' efforts can shift away from data management and utility software development to scientific research and discovery An integrated view of these activities is provided by the notion of scientific workflows - a series of structured activities and computations that arise in scientific problem-solving. An information technology framework that supports scientific workflows is the Ptolemy II based environment called Kepler. This paper discusses the issues associated with practical automation of scientific processes and workflows and illustrates this with workflows developed using the Kepler framework and tools.
Convolutional Neural Network Based dem Super Resolution
NASA Astrophysics Data System (ADS)
Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang
2016-06-01
DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.
NASA Technical Reports Server (NTRS)
Jani, Yashvant
1992-01-01
As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
Complex intuitionistic fuzzy sets
NASA Astrophysics Data System (ADS)
Alkouri, Abdulazeez (Moh'd. Jumah) S.; Salleh, Abdul Razak
2012-09-01
This paper presents a new concept of complex intuitionistic fuzzy set (CIFS) which is generalized from the innovative concept of a complex fuzzy set (CFS) by adding the non-membership term to the definition of CFS. The novelty of CIFS lies in its ability for membership and non-membership functions to achieve more range of values. The ranges of values are extended to the unit circle in complex plane for both membership and non-membership functions instead of [0, 1] as in the conventional intuitionistic fuzzy functions. We define basic operations namely complement, union, and intersection on CIFSs. Properties of these operations are derived.
Fuzzy blood pressure measurement
NASA Astrophysics Data System (ADS)
Cuce, Antonino; Di Guardo, Mario; Sicurella, Gaetano
1998-10-01
In this paper, an intelligent system for blood pressure measurement is posed together with a possible implementation using an eight bit fuzzy processor. The system can automatically determine the ideal cuff inflation level eliminating the discomfort and misreading caused by incorrect cuff inflation. Using statistics distribution of the systolic and diastolic blood pressure, in the inflation phase, a fuzzy rule system determine the pressure levels at which checking the presence of heart beat in order to exceed the systolic pressure with the minimum gap. The heart beats, characterized through pressure variations, are recognized by a fuzzy classifier.
NASA Astrophysics Data System (ADS)
Bosch, David; Ledo, Juanjo; Queralt, Pilar
2013-07-01
Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.
Network-based prediction of protein function
Sharan, Roded; Ulitsky, Igor; Shamir, Ron
2007-01-01
Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community. PMID:17353930
Spectral Identification Inference Engine
Energy Science and Technology Software Center (ESTSC)
2004-07-27
The software interprets spectra (mass spectra, ion mobility spectra, etc.) using a method that mimics how an expert human analyst would perform the interpretation. Because spectra can be described linguistically (e.g. peak X must be large and peak y must be small), their description can be reduced to rules using fuzzy logic. Therefore, a fuzzy logic rule base can be applied to interpreting the spectra. The fuzzy logic rule base is also easy for themore » user to understand, and therefore, easy to check and verify its accuracy.« less