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239
Accuracybased Neuro and NeuroFuzzy Classifier Systems
 IN
, 2002
"... Learning Classifier Systems traditionally use a binary representation with wildcards added to allow for generalizations over the problem encoding. However, the simple scheme can be limiting in complex domains. In this paper we present results from the use of neural networkbased representation schem ..."
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Cited by 25 (6 self)
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Learning Classifier Systems traditionally use a binary representation with wildcards added to allow for generalizations over the problem encoding. However, the simple scheme can be limiting in complex domains. In this paper we present results from the use of neural networkbased representation schemes within the accuracybased XCS. Here each rule's condition and action are represented by a small neural network, evolved through the actions of the genetic algorithm. After describing the changes required to the standard production system functionality, optimal performance is presented using multilayered perceptrons to represent the individual rules. Results from the use of fuzzy logic through radial basis fuction networks are then presented. In particular, the new representation scheme is shown to produce systems where outputs are a function of the inputs.
A Neurofuzzy scheme for simultaneous feature selection and fuzzy rulebased classification”,IEEE
 Transactions on Neural Networks
, 2004
"... Abstract—Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neurofuzzy scheme for designing a classifier along with feature selection. It is a fourlayered feedforward network for realiz ..."
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Cited by 24 (1 self)
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Abstract—Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neurofuzzy scheme for designing a classifier along with feature selection. It is a fourlayered feedforward network for realizing a fuzzy rulebased classifier. The network is trained by error backpropagation in three phases. In the first phase, the network learns the important features and the classification rules. In the subsequent phases, the network is pruned to an “optimal ” architecture that represents an “optimal” set of rules. Pruning is found to drastically reduce the size of the network without degrading the performance. The pruned network is further tuned to improve performance. The rules learned by the network can be easily read from the network. The system is tested on both synthetic and real data sets and found to perform quite well. Index Terms—Classification, feature analysis, neurofuzzy systems, rule extraction. I.
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 22 (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.
Evolving Fuzzy Rules for Breast Cancer Diagnosis
, 1998
"... We present an evolutionary approach for discovering fuzzy systems for breast cancer diagnosis. By judiciously designing an appropriate representation scheme (genome) and fitness function, the genetic algorithm is then able to produce successful systems. These surpass the best known systems to date i ..."
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Cited by 19 (9 self)
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We present an evolutionary approach for discovering fuzzy systems for breast cancer diagnosis. By judiciously designing an appropriate representation scheme (genome) and fitness function, the genetic algorithm is then able to produce successful systems. These surpass the best known systems to date in terms of combined performance and simplicity. I. Introduction Fuzzy logic is a computational paradigm that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning [1]. A prime characteristic of fuzzy logic is its capability of expressing knowledge in a linguistic way, allowing a system to be described by simple, "humanfriendly" rules. A fuzzy inference system is a rulebased system that uses fuzzy logic, rather than boolean logic, to reason about data [1]. Its basic structure comprises four main components: (1) a fuzzifier, which translates crisp (realvalued) inputs into fuzzy values, (2) an inference engine that applies a fuzzy reaso...
Fuzzy Inference Systems Implemented on Neural Architectures for Motor Fault Detection and Diagnosis
 IEEE Transactions on Industrial Electronics
, 1999
"... Abstract—Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault det ..."
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Cited by 17 (3 self)
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Abstract—Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, namely, Fuzzy Adaptive Learning Control/Decision Network (FALCON) and Adaptive Network
Minimal Fuzzy Memberships and Rules Using Hierarchical Genetic Algorithms
 IEEE Trans. Ind. Electron
, 1998
"... Abstract—A new scheme to obtain optimal fuzzy subsets and rules is proposed. The method is derived from the use of genetic algorithms, where the genes of the chromosome are classified into two different types. These genes can be arranged in a hierarchical form, where one type of genes controls the o ..."
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Cited by 17 (5 self)
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Abstract—A new scheme to obtain optimal fuzzy subsets and rules is proposed. The method is derived from the use of genetic algorithms, where the genes of the chromosome are classified into two different types. These genes can be arranged in a hierarchical form, where one type of genes controls the other type of genes. The effectiveness of this genetic formulation enables the fuzzy subsets and rules to be optimally reduced and, yet, the system performance is well maintained. In this paper, the details of formulation of the genetic structure are given. The required procedures for coding the fuzzy membership function and rules into the chromosome are also described. To justify this approach to fuzzy logic design, the proposed scheme is applied to control a constant water pressure pumping system. The obtained results, as well as the associated final fuzzy subsets, are included in this paper. Because of its simplicity, the method could lead to a potentially lowcost fuzzy logic implementation. Index Terms—DNA, fuzzy control, genetic algorithms. I.
A Comparative Study of Neural Network Structures in Identification of Nonlinear Systems
 Mechatronics
, 1999
"... This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), RungeKutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identificat ..."
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Cited by 15 (8 self)
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This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), RungeKutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a three degrees of freedom anthropomorphic robotic manipulator.
Learning Membership Functions In A FunctionBased Object Recognition System
 Journal of Artificial Intelligence Research
, 1995
"... Functionalitybased object recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function of the category. Such systems naturally associate a "measure of goodness" or "membership value" with a recognized object. T ..."
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Cited by 15 (0 self)
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Functionalitybased object recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function of the category. Such systems naturally associate a "measure of goodness" or "membership value" with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value for a primitive evaluation of a particular physical property of an object. In previous versions of a recognition system known as GRUFF, the membership function for each of the primitive evaluations had to be handcrafted by the system designer. In this paper, we provide a learning component for the GRUFF system, called Omlet, that automatically learns primitive evaluation membership functions given a set of example objects labeled with their desired category measure. T...
Implementation of CMOS Fuzzy Controllers as MixedSignal Integrated Circuits
, 1997
"... : This paper discusses architectural and circuitlevel aspects related to hardware realizations of fuzzy controllers. A brief overview on fuzzy inference methods is given focusing on chip implementation. The singleton or zeroorder Sugeno's method is chosen since it offers a good tradeoff betw ..."
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Cited by 15 (9 self)
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: This paper discusses architectural and circuitlevel aspects related to hardware realizations of fuzzy controllers. A brief overview on fuzzy inference methods is given focusing on chip implementation. The singleton or zeroorder Sugeno's method is chosen since it offers a good tradeoff between hardware simplicity and control efficiency. The CMOS microcontroller described herein processes information in the currentdomain, but inputoutput signals are represented as voltage to ease communications with conventional control circuitry. Programming functionalities are added by combining analog and digital techniques, giving rise to a versatile microcontroller, capable of solving different control problems. After identifying the basic component blocks, the circuits used for their implementation are discussed and compared with other alternatives. This study is illustrated with the experimental results of prototypes integrated in different CMOS technologies. I. INTRODUCTION At present, mos...
Fuzzy Identification from a Grey Box Modeling Point of View
 Fuzzy Model Identification
, 1997
"... Introduction The design of mathematical models of complex realworld (and typically nonlinear) systems is essential in many fields of science and engineering. The developed models can be used, e.g., to explain the behavior of the underlying system as well as for prediction and control purposes. A c ..."
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Cited by 14 (0 self)
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Introduction The design of mathematical models of complex realworld (and typically nonlinear) systems is essential in many fields of science and engineering. The developed models can be used, e.g., to explain the behavior of the underlying system as well as for prediction and control purposes. A common approach for building mathematical models is socalled black box modeling (Ljung, 1987; Soderstrom and Stoica, 1989), as opposed to more traditional physical modeling (or white box modeling), where everything is considered known a priori from physics. Strictly speaking, a black box model is designed entirely from data using no physical or verbal insight whatsoever. The structure of the model is chosen from families that are known to be very flexible and successful in past applications. This also means that the model parameters lack physical or verbal significance; they are tuned just to fit the observed data as well as possible. The term "black box modeling" is