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ANFIS: AdaptiveNetworkBased Fuzzy Inference System”,
 IEEE Trans. on System, Man and Cybernetics,
, 1993
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Neurofuzzy modeling and control
 IEEE PROCEEDINGS
, 1995
"... Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of ad ..."
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Cited by 239 (1 self)
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Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed.
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 169 (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.
SelfLearning Fuzzy Controllers Based on Temporal Back Propagation
, 1992
"... This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a nearoptimal manner. This methodology, termed temporal back propagation, is modelinsensitive in the sense that it can deal with plants tha ..."
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Cited by 98 (3 self)
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This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a nearoptimal manner. This methodology, termed temporal back propagation, is modelinsensitive in the sense that it can deal with plants that can be represented in a piecewise differentiable format, such as difference equations, neural networks, GMDH, fuzzy models, etc. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy ifthen rules obtained from human experts, or automatically derive the fuzzy ifthen rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller. 1 Introduction Fuzzy controllers (FC's) have recently found various applications in industry as well as in household appliances. For com...
Structure Determination in Fuzzy Modeling: A Fuzzy CART Approach
, 1994
"... This paper presents an innovative approach to the structure determination problem in fuzzy modeling. By using the wellknown CART (classification and regression tree) algorithm as a quick preprocess, the proposed method can roughly estimate the structure (numbers of membership functions and number o ..."
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Cited by 21 (2 self)
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This paper presents an innovative approach to the structure determination problem in fuzzy modeling. By using the wellknown CART (classification and regression tree) algorithm as a quick preprocess, the proposed method can roughly estimate the structure (numbers of membership functions and number of fuzzy rules, etc.) of a fuzzy inference system; then the parameter identification is carried out by the hybrid learning scheme developed in our previous work [3, 2, 5]. Morevoer, the identified fuzzy inference system has the property that the total of firing strengths is always equal to one; this speeds up learning processes and reduces roundoff errors. 1 Introduction Fuzzy modeling [11, 10] is a new branch of system identification which concerns with the construction of a fuzzy inference system (or fuzzy model) that can predict and hopefully explain the behavior of an unknown system described by a set of sample data. Two primary tasks of fuzzy modeling are structure determination...
Neural networks and structured knowledge: Rule extraction and applications
 Applied Intelligence
, 2000
"... Abstract. As the second part of a special issue on “Neural Networks and Structured Knowledge, ” the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of st ..."
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Cited by 6 (1 self)
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Abstract. As the second part of a special issue on “Neural Networks and Structured Knowledge, ” the contributions collected here concentrate on the extraction of knowledge, particularly in the form of rules, from neural networks, and on applications relying on the representation and processing of structured knowledge by neural networks. The transformation of the lowlevel internal representation in a neural network into higherlevel knowledge or information that can be interpreted more easily by humans and integrated with symboloriented mechanisms is the subject of the first group of papers. The second group of papers uses specific applications as starting point, and describes approaches based on neural networks for the knowledge representation required to solve crucial tasks in the respective application.
Hybrid Soft Computing Systems: A Critical Survey with Engineering Applications
"... During the last decade the human behaviour and human imitating processing methods have become of central interest through the scientific community. The development of methods that mimic the human learning process being able to solve complex engineering problems which are difficult to deal with vi ..."
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Cited by 1 (1 self)
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During the last decade the human behaviour and human imitating processing methods have become of central interest through the scientific community. The development of methods that mimic the human learning process being able to solve complex engineering problems which are difficult to deal with via conventional approaches, seems to be on an immediate emergency. Concepts such as nervous system, fuzziness and evolution come directly from human resources enclosing attractive properties and reach theory, and as a consequence lead to new scientific horizons. In this direction, soft computing indicates a new family of computing techniques that accommodate human computing resources and make them being utilized. Neural networks, fuzzy systems and genetic algorithms are mainly the three basic constituents that contribute to this juncture. Starting with the basic features in each one of these partners, this paper is focused on the examination of all the possible combined (hybrid) meth...
LMTI, Faculté des sciences,
"... An Adaptative NeuroFuzzy Inference System (ANFIS) is developed to predict the acoustic form function (FF) for an infinite length cylindrical shell excited perpendicularly to its axis. The WignerVille distribution (WVD) is used like a comparison tool between the calculated FF by the analytical meth ..."
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An Adaptative NeuroFuzzy Inference System (ANFIS) is developed to predict the acoustic form function (FF) for an infinite length cylindrical shell excited perpendicularly to its axis. The WignerVille distribution (WVD) is used like a comparison tool between the calculated FF by the analytical method and that predicted by the neurofuzzy technique for a copper tube. During the application of this technique, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). This neurofuzzy technique is able to predict the FF with a mean relative error (MRE) about 1.7%. Keywords—ANFIS, acoustic scattering, cylindrical shells, WignerVille distribution. 1.
MAAREF
"... Contribution à l’étude, à la conception et à la mise en œuvre de stratégie de contrôle intelligent distribué en robotique collective ..."
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Contribution à l’étude, à la conception et à la mise en œuvre de stratégie de contrôle intelligent distribué en robotique collective