Librarian View
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20200724064536.0
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121227s1997 gw | s |||| 0|eng d
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9783642607677
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978-3-642-60767-7
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7
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10.1007/978-3-642-60767-7
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doi
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(DE-He213)978-3-642-60767-7
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QA76.9.C65
072
7
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UGK
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bicssc
072
7
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COM072000
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bisacsh
082
0
4
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003.3
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23
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Hellendoorn, Hans.
245
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Fuzzy Model Identification
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[electronic resource] :
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Selected Approaches /
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edited by Hans Hellendoorn, Dimiter Driankov.
260
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Berlin, Heidelberg :
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Springer Berlin Heidelberg,
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1997.
300
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1 online resource.
505
0
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<B>Form the contents:</B> Introduction -- General Overview -- Clustering Methods -- Neural Networks -- Genetic Algorithms -- Artificial Intelligence.
506
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Access restricted by licensing agreement.
520
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This carefully edited volume presents a collection of recent works in fuzzy model identification. It opens the field of fuzzy identification to conventional control theorists as a complement to existing approaches, provides practicing control engineers with the algorithmic and practical aspects of a set of new identification techniques, and emphasizes opportunities for a more systematic and coherent theory of fuzzy identification by bringing together methods based on different techniques but aiming at the identification of the same types of fuzzy models.<BR> In control engineering, mathematical models are often constructed, for example based on differential or difference equations or derived from physical laws without using system data (white-box models) or using data but no insight (black-box models). In this volume the authors choose a combination of these models from types of structures that are known to be flexible and successful in applications. They consider Mamdani, Takagi-Sugeno, and singleton models, employing such identification methods as clustering, neural networks, genetic algorithms, and classical learning.<BR> All authors use the same notation and terminology, and each describes the model to be identified and the identification technique with algorithms that will help the reader to apply the presented methods in his or her own environment to solve real-world problems. Furthermore, each author gives a practical example to show how the presented method works, and deals with the issues of prior knowledge, model complexity, robustness of the identification method, and real-world applications.
590
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Access is available to the Yale community.
650
0
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Computer science.
650
0
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Microprogramming.
650
0
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Artificial intelligence.
650
0
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Computer simulation.
650
0
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Optical pattern recognition.
650
0
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Computer aided design.
700
1
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Driankov, Dimiter.
710
2
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SpringerLink (Online service)
730
0
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Springer ebooks.
776
0
8
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Printed edition:
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9783540627210
852
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0
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Online resource
856
4
0
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Online book
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https://yale.idm.oclc.org/login?URL=http://dx.doi.org/10.1007/978-3-642-60767-7
901
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QA76.9.C65
902
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Yale Internet Resource
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Yale Internet Resource >> None|DELIM|11394089
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online resource
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2013-02-05T15:28:37.000Z
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DO NOT EDIT. DO NOT EXPORT.
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4
0
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http://dx.doi.org/10.1007/978-3-642-60767-7