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020 _a9781447150138
_9978-1-4471-5013-8
024 7 _a10.1007/978-1-4471-5013-8
_2doi
035 _ato000483479
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKruse, Rudolf.
_eauthor.
_9323724
245 1 0 _aComputational Intelligence
_h[electronic resource] :
_bA Methodological Introduction /
_cby Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held.
260 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXI, 488 p. 234 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aTexts in Computer Science,
_x1868-0941
505 0 _aIntroduction -- Part I: Neural Networks -- Introduction -- Threshold Logic Units -- General Neural Networks -- Multi-Layer Perceptrons -- Radial Basis Function Networks -- Self-Organizing Maps -- Hopfield Networks -- Recurrent Networks -- Mathematical Remarks -- Part II: Evolutionary Algorithms -- Introduction to Evolutionary Algorithms -- Elements of Evolutionary Algorithms -- Fundamental Evolutionary Algorithms -- Special Applications and Techniques -- Part III: Fuzzy Systems -- Fuzzy Sets and Fuzzy Logic -- The Extension Principle -- Fuzzy Relations -- Similarity Relations -- Fuzzy Control -- Fuzzy Clustering -- Part IV: Bayes Networks -- Introduction to Bayes Networks -- Elements of Probability and Graph Theory -- Decompositions -- Evidence Propagation -- Learning Graphical Models.
520 _aComputational intelligence (CI) encompasses a range of nature-inspired methods that exhibit intelligent behavior in complex environments. This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Topics and features: Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools Contains numerous examples and definitions throughout the text Presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks Covers the latest approaches, including ant colony optimization and probabilistic graphical models Written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry Students of computer science will find the text a must-read reference for courses on artificial intelligence and intelligent systems. The book is also an ideal self-study resource for researchers and practitioners involved in all areas of CI.
650 0 _aComputer Science.
_9155490
650 0 _aArtificial intelligence.
_9274099
650 0 _aEngineering mathematics.
_9303575
650 1 4 _aComputer Science.
_9155490
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
_9303577
700 1 _aBorgelt, Christian.
_eauthor.
_9323931
700 1 _aKlawonn, Frank.
_eauthor.
_9320886
700 1 _aMoewes, Christian.
_eauthor.
_9413737
700 1 _aSteinbrecher, Matthias.
_eauthor.
_9413738
700 1 _aHeld, Pascal.
_eauthor.
_9413739
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aTexts in Computer Science,
_9306623
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-5013-8
912 _aZDB-2-SCS
999 _c356315