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Titlebook: Computational Intelligence in Theory and Practice; Bernd Reusch,Karl-Heinz Temme Conference proceedings 2001 Springer-Verlag Berlin Heidel

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书目名称Computational Intelligence in Theory and Practice
编辑Bernd Reusch,Karl-Heinz Temme
视频videohttp://file.papertrans.cn/233/232540/232540.mp4
丛书名称Advances in Intelligent and Soft Computing
图书封面Titlebook: Computational Intelligence in Theory and Practice;  Bernd Reusch,Karl-Heinz Temme Conference proceedings 2001 Springer-Verlag Berlin Heidel
描述Computational Intelligence with its roots in Fuzzy Logic, Neural Networks and Evolutionary Algorithms has become an important research and application field in computer science in the last decade. Methodologies from these areas and combinations of them enable users from engineering, business, medicine and many more branches to capture and process vague, incomplete, uncertain and imprecise data and knowledge. Many algorithms and tools have been developed to solve problems in the realms of high and low level control, information processing, diagnostics, decision support, classification, optimisation and many more. This book tries to show the impact and feedback between theory and applications of Computational Intelligence, highlighted on selected examples.
出版日期Conference proceedings 2001
关键词Computational Intelligence; Evolution; Fuzzy Clustering; Fuzzy Data Models and Bases; Fuzzy Logic Theory
版次1
doihttps://doi.org/10.1007/978-3-7908-1831-4
isbn_softcover978-3-7908-1357-9
isbn_ebook978-3-7908-1831-4Series ISSN 1867-5662 Series E-ISSN 1867-5670
issn_series 1867-5662
copyrightSpringer-Verlag Berlin Heidelberg 2001
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Proof Theory of Many-Valued Logic and Linear Optimizationte-valued and to a large class of infinite-valued logics, for instance to Lukasiewicz and Gödel logics. The resulting reduction is deterministic and has linear cost which makes it feasible to implement satisfiability checking in such logics via linear optimization methods. The reduction is based on
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Triangular Norms — An Overviewss the algebraic properties of t-norms. Different construction methods including latest ones are described. A special attention is paid to the representation of continuous t-norms. Some related operations and fields of applications are discussed, too. An exhaustive bibliography is included.
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A First View on the Alternatives of Fuzzy Set Theory however a lot of new models have been introduced for handling incomplete information. Undoubtly fuzzy set theory initiated by Zadeh [1] in 1965 plays the central role. Besides this widely applied theory, many other models pretending to be competitive with fuzzy set theory have been launched: rough
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Generalizing the Explicit Concept of Rough Set on the Basis of Modal Logicns with respect to a fixed equivalence relation on the universe considered..In contrast to this approach we are of the opinion that a rough set is an unknown (or non-deterministic) set which generates a certain lower and upper approximation. Now, one is faced with the fact that different sets can ge
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Probalistic Networks and Fuzzy Clustering as Generalizations of Naive Bayes Classifierse seen as generalizations of naive Bayes classifiers. If all attributes are numeric (except the class attribute, of course), naive Bayes classifiers often assume an axis-parallel multidimensional normal distribution for each class as the underlying model. Probabilistic networks remove the requiremen
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Genetic Optimization of Fuzzy Classification Systems — A Case Study an inspection system for a silk-screen printing process. The classification algorithm is applied to a reference image in the initial step of the printing process in order to obtain regions which are to be checked by applying different criteria. Tight limitations in terms of computation speed have n
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