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ANFIS: AdaptiveNetworkBased Fuzzy Inference System
, 1993
"... This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an inputoutput mapping bas ..."
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Cited by 432 (5 self)
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This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an inputoutput mapping based on both human knowledge (in the form of fuzzy ifthen rules) and stipulated inputoutput data pairs. In our simulation, we employ the ANFIS architecture to model nonlinear functions, identify nonlinear components onlinely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificail neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. 1 Introduction System modeling based on conventional mathematical tools (e.g., differential equations) is not well suited for dealing with illdefine...
Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems
, 1993
"... This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational ..."
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Cited by 126 (4 self)
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This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational power, etc., can be applied to both models directly. It is of interest to observe that twomodels stemming from different origins turn out to be functional equivalent.
Designing fuzzy inference systems from data: an interpretabilityoriented review
 IEEE Trans. Fuzzy Systems
"... Abstract—Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules infer ..."
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Cited by 49 (8 self)
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Abstract—Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for humancomputer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. This paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined. Index Terms—Fuzzy inference systems, fuzzy partitioning, interpretability, rule induction, system optimization. I.
Neural Networks in Designing Fuzzy Systems for Real World Applications”, Fuzz y
 University of Sydney
, 1995
"... AbstractA special multilayer perceptron architecture known as FuNe I is successfully used for generating fuzzy systems for a number of real world applications. The FuNe I trained with supervised learning can be used to extract fuzzy rules from a given representative input/output data set. Furthermo ..."
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Cited by 46 (8 self)
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AbstractA special multilayer perceptron architecture known as FuNe I is successfully used for generating fuzzy systems for a number of real world applications. The FuNe I trained with supervised learning can be used to extract fuzzy rules from a given representative input/output data set. Furthermore, optimization of the knowledge base is possible including the tuning of membership functions. The new method employed to identify the rule relevant nodes before the rules are extracted makes FuNe I suitable for applications with large number of inputs. Some of the real world applicationsinareas of state identi cation and image classi cation show encouraging results in a shorter development time. Expert knowledge is not compulsory but can be included in the automatically extracted knowledge base. The generated fuzzy system can be implemented in hardware very easily. A exible prototype board is developed with a FPGA chip in order to run applications with up to 128 inputs and 4 outputs in realtime (1.25 million rules per second).
Operating regime based process modeling and identification ph.d thesis
"... the Department of Engineering Cybernetics, who has been of great inspiration and support. Thanks. Moreover, I would like to thank Prof. Petros Ioannou at the UniversityofSouthern California for hosting my six month visit at USC. My interactions with him and his students improved my mathematical prec ..."
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Cited by 26 (12 self)
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the Department of Engineering Cybernetics, who has been of great inspiration and support. Thanks. Moreover, I would like to thank Prof. Petros Ioannou at the UniversityofSouthern California for hosting my six month visit at USC. My interactions with him and his students improved my mathematical precision and resulted in some adaptive control results that are partially reported in this thesis. Two chapters in this thesis are based on manuscripts that are coauthored with Aage V. S rensen at
Combined Genetic Algorithm Optimization and Regularized Orthogonal Least Squares Learning for Radial Basis Function Networks
, 1999
"... The paper presents a twolevel learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimiz ..."
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Cited by 25 (6 self)
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The paper presents a twolevel learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.
The Shape of Fuzzy Sets in Adaptive Function Approximation
, 2001
"... The shape of ifpart fuzzy sets affects how well feedforward fuzzy systems approximate continuous functions. We explore a wide range of candidate ifpart sets and derive supervised learning laws that tune them. Then we test how well the resulting adaptive fuzzy systems approximate a battery of test ..."
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Cited by 19 (3 self)
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The shape of ifpart fuzzy sets affects how well feedforward fuzzy systems approximate continuous functions. We explore a wide range of candidate ifpart sets and derive supervised learning laws that tune them. Then we test how well the resulting adaptive fuzzy systems approximate a battery of test functions. No one set shape emerges as the best shape. The sinc function often does well and has a tractable learning law. But its undulating sidelobes may have no linguistic meaning. This suggests that the engineering goal of functionapproximation accuracy may sometimes have to outweigh the linguistic or philosophical interpretations of fuzzy sets that have accompanied their use in expert systems. We divide the ifpart sets into two large classes. The first class consists ofdimensional joint sets that factor into scalar sets as found in almost all published fuzzy systems. These sets ignore the correlations among vector components of input vectors. Fuzzy systems that use factorable ifpart sets suffer in general from exponential rule explosion in high dimensions when they blindly approximate functions without knowledge of the functions. The factorable fuzzy sets themselves also suffer from what we call the second curse of dimensionality: The fuzzy sets tend to become binary spikes in high dimension. The second class of ifpart sets consists of the more general but less commondimensional joint sets that do not factor into scalar fuzzy sets. We present a method for constructing such unfactorable joint sets from scalar distance measures. Fuzzy systems that use unfactorable ifpart sets need not suffer from exponential rule explosion but their increased complexity may lead to intractable learning laws and inscrutable ifthen rules. We prove that some of these unfactorable join...
Fuzzy Rule Based Modeling As A Universal Approximation Tool
, 1996
"... INTRODUCTION 1.1. WHY UNIVERSAL APPROXIMATION In some cases, fuzzy rule based model needs tuning. If we have applied some version of fuzzy rule based modeling, and the resulting model is satisfactory, great. But sometimes, the resulting model is not of very high quality: \Gamma we may have misint ..."
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Cited by 16 (12 self)
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INTRODUCTION 1.1. WHY UNIVERSAL APPROXIMATION In some cases, fuzzy rule based model needs tuning. If we have applied some version of fuzzy rule based modeling, and the resulting model is satisfactory, great. But sometimes, the resulting model is not of very high quality: \Gamma we may have misinterpreted some of the expert's rules; \Gamma we may have missed some of the important rules. So, to improve the quality of the resulting model, we must either: \Gamma reinterpret the existing rules, or \Gamma ask the experts for extra rules. 2 V. KREINOVICH ET AL. Before we start tuning, we want to make sure that tuning will help. In order to make sure that this "tuning" will always help, we must guarantee that by appropriate tuning, we will be able to change the initial (not very successful) model into a successful one. To guarantee that the fuzzy rule based modeling me
Fuzzy RuleBased Networks for Control
 IEEE Transactions on Fuzzy Systems
, 1994
"... We present a method for the learning of fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a threestep approach to building a complete function approximation ..."
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Cited by 16 (0 self)
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We present a method for the learning of fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a threestep approach to building a complete function approximation system: first, learning the membership functions and creating a cellbased rule representation; second, simplifying the cellbased rules using an informationtheoretic approach for induction of rules from discretevalued data; and finally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backerupper control system. I. Introduction The problem of approximating a function from a set of examples can be solved in a multitude of ways, including mathematicalmethods using an explicit model for the function to be learned and mo...
A systematic approach to a selfgenerating fuzzy ruletable for function approximation
 IEEE Trans Syst., Man, Cybern
, 2000
"... Abstract—In this paper, a systematic design is proposed to determine fuzzy system structure and learning its parameters, from a set of given training examples. In particular, two fundamental problems concerning fuzzy system modeling are addressed: 1) fuzzy rule parameter optimization and 2) the iden ..."
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Cited by 16 (10 self)
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Abstract—In this paper, a systematic design is proposed to determine fuzzy system structure and learning its parameters, from a set of given training examples. In particular, two fundamental problems concerning fuzzy system modeling are addressed: 1) fuzzy rule parameter optimization and 2) the identification of system structure (i.e., the number of membership functions and fuzzy rules). A fourstep approach to build a fuzzy system automatically is presented: Step 1 directly obtains the optimum fuzzy rules for a given membership function configuration. Step 2 optimizes the allocation of the membership functions and the conclusion of the rules, in order to achieve a better approximation. Step 3 determines a new and more suitable topology with the information derived from the approximation error distribution; it decides which variables should increase the number of membership functions. Finally, Step 4 determines which structure should be selected to approximate the function, from the possible configurations provided by the algorithm in the three previous steps. The results of applying this method to the problem of function approximation are presented and then compared with other methodologies proposed in the bibliography. Index Terms—Function approximation, fuzzy system construction, fuzzy system design, knowledge acquisition. I.