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Titlebook: Intelligent Hybrid Systems; Fuzzy Logic, Neural Da Ruan Book 1997 Springer Science+Business Media New York 1997 Chaos.algorithms.filtering

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Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms for the following chapters. Focus is placed on (1) the similarities between the three technologies through the common keyword of . and (2) how to use these technologies at a practical or programming level.
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A Fuzzy Neural Network for Approximate Fuzzy Reasoningployed to highlight an approximation to the conventional fuzzy reasoning approach which considerably simplifies the resulting architecture. The performance of the fuzzy neural network is demonstrated by its application to three benchmark problems: nonlinear function approximation; on-line identifica
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Novel Neural Algorithms for Solving Fuzzy Relation Equations easily and strict theoretical analyses on convergence of algorithms are not given. To overcome these problems, we present in this chapter some novel neural algorithms based on fuzzy δ rules. We first describe such algorithms for max-min operator networks, then we demonstrate that these algorithms c
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Methods for Simplification of Fuzzy Models may manifest itself in the form of a larger number of rules than necessary, or in the form of fuzzy sets that are very similar to one another. By reducing this redundancy, transparent fuzzy models with appropriate number of rules and distinct fuzzy sets are obtained. This chapter considers cluster
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Data-Driven Identification of Key Variablesfic partition of this data set. This problem has a broad applicability, even though it emerged in the context of a particular engineering application—the process of manufacturing electric circuit boards..Two distinct approaches are used for dealing with the problem, each resulting in a particular al
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