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Titlebook: Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications; Okyay Kaynak,Lotfi A. Zadeh,Imre J. Rudas Confer

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發(fā)表于 2025-3-21 16:26:14 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications
編輯Okyay Kaynak,Lotfi A. Zadeh,Imre J. Rudas
視頻videohttp://file.papertrans.cn/233/232562/232562.mp4
叢書名稱NATO ASI Subseries F:
圖書封面Titlebook: Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications;  Okyay Kaynak,Lotfi A. Zadeh,Imre J. Rudas Confer
描述Soft computing is a consortium of computing methodologies that provide a foundation for the conception, design, and deployment of intelligent systems and aims to formalize the human ability to make rational decisions in an environment of uncertainty and imprecision. This book is based on a NATO Advanced Study Institute held in 1996 on soft computing and its applications. The distinguished contributors consider the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications. Two areas emphasized in the book are how to achieve a synergistic combination of the main constituents of soft computing and how the combination can be used to achieve a high Machine Intelligence Quotient.
出版日期Conference proceedings 1998
關(guān)鍵詞Cyc; automation; cognition; computational intelligence; computer vision; data analysis; decision support s
版次1
doihttps://doi.org/10.1007/978-3-642-58930-0
isbn_softcover978-3-642-63796-4
isbn_ebook978-3-642-58930-0Series ISSN 0258-1248
issn_series 0258-1248
copyrightSpringer-Verlag Berlin Heidelberg 1998
The information of publication is updating

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https://doi.org/10.1007/978-3-642-75585-9nguistic form and then transformed into computational expressions through at least two sets of major transformations stated above, i.e., first from language to formulae next from formulae to numbers..In this context, fuzzy normal-form formulae of linguistic expressions are derived with the construct
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H. I. Ansoff,R. P. Declerck,R. L. Hayestions on [0,1] with identity elements but they are not monotonic. Simulations have been carried out so as to determine the effects of these new operators on the performance of the fuzzy controllers. It is concluded that the performance of the fuzzy controller can be improved by using some sets of ge
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Unternehmensethik und strategische Planungneuron model is a Heaviside fixed function. In this framework the supervised learning is direct, that is to say without recursive algorithms for computing the weights and threshold, related to the new foundation of the threshold logic by Resconi and Raymondi. This paper will review the main aspects
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H. I. Ansoff,R. P. Declerck,R. L. Hayesituation previously considered disorder [.] and can learn how to choose among functions the function that best approximate the supervisor’s response [.]. The instrument to perceive order is the ., whose elements are the . (MF), the . (MRS) the . (MS) and the . (MEF). To learn and perceive order the
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Corporate Strategy: The Core Concepts study, the best of the six schemes in one sense; it finds 11 prototypes that yield a resubstitution error rate of 0. In a different sense, the DR method is best, yielding a classifier that commits only 3 errors with 5 prototypes.
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Computational Intelligence Defined - By Everyone !,tional system. These issues are examined with a view towards guessing how best to integrate and exploit the promise of the neural approach with other efforts aimed at advancing the art and science of pattern recognition and its applications in fielded systems in the next decade. A further purpose of
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