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Titlebook: Neuro-Fuzzy Architectures and Hybrid Learning; Danuta Rutkowska Book 2002 Springer-Verlag Berlin Heidelberg 2002 Artificial Intelligence.F

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發(fā)表于 2025-3-21 18:01:51 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neuro-Fuzzy Architectures and Hybrid Learning
編輯Danuta Rutkowska
視頻videohttp://file.papertrans.cn/664/663792/663792.mp4
概述Novel neuro-fuzzy architectures and hybrid learning algorithms.Overview of early and latest results concerning neural networks and fuzzy sets and systems.Includes supplementary material:
叢書名稱Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Neuro-Fuzzy Architectures and Hybrid Learning; Danuta Rutkowska Book 2002 Springer-Verlag Berlin Heidelberg 2002 Artificial Intelligence.F
描述The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe- matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ- ence of the human mind as a role model is clearly visible in the methodolo- gies which have emerged, mainly during the past two decades, for the con- ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively pr
出版日期Book 2002
關(guān)鍵詞Artificial Intelligence; Fuzzy; Fuzzy Systems; Learning Algorithms; Neural Networks; Neuro-Fuzzy Systems;
版次1
doihttps://doi.org/10.1007/978-3-7908-1802-4
isbn_softcover978-3-7908-2500-8
isbn_ebook978-3-7908-1802-4Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 2002
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Hybrid Learning Methods,nt, genetic, and clustering algorithms. These methods are first described, and then the hybrid algo rithms for rule generation and parameter tuning are presented, including the algorithms proposed in [479], [438], [439], [440], [480], [481].
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Neural Networks and Neuro-Fuzzy Systems,into neural networks [169], and . (see Section 3.3), which are representations of . in the form of connectionist networks [513], similar to neural networks. Of course, different types of neuro-fuzzy systems can be found in the literature, e.g. [493], [300], [53], [162], [361], [243], [347], [582], [229], [223], [496], [244], [56], [141], [101].
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Description of Fuzzy Inference Systems,zzy systems have been recently combined with neural networks and genetic algorithms to create different kinds of neuro-fuzzy systems and intelligent systems. This chapter presents an overview of fuzzy sets, approximate reasoning, and fuzzy systems.
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