000 04169nam a22005655i 4500
001 978-1-4471-5013-8
003 DE-He213
005 20200421112224.0
007 cr nn 008mamaa
008 130328s2013 xxk| s |||| 0|eng d
020 _a9781447150138
_9978-1-4471-5013-8
024 7 _a10.1007/978-1-4471-5013-8
_2doi
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.
245 1 0 _aComputational Intelligence
_h[electronic resource] :
_bA Methodological Introduction /
_cby Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXII, 492 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aArtificial intelligence.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
700 1 _aBorgelt, Christian.
_eauthor.
700 1 _aKlawonn, Frank.
_eauthor.
700 1 _aMoewes, Christian.
_eauthor.
700 1 _aSteinbrecher, Matthias.
_eauthor.
700 1 _aHeld, Pascal.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447150121
830 0 _aTexts in Computer Science,
_x1868-0941
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-5013-8
912 _aZDB-2-SCS
942 _cEBK
999 _c57569
_d57569