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001 978-1-84628-303-1
003 DE-He213
005 20161121231114.0
007 cr nn 008mamaa
008 100301s2006 xxk| s |||| 0|eng d
020 _a9781846283031
_9978-1-84628-303-1
024 7 _a10.1007/1-84628-303-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aDu, K. -L.
_eauthor.
245 1 0 _aNeural Networks in a Softcomputing Framework
_h[electronic resource] /
_cby K. -L. Du, M. N. S. Swamy.
264 1 _aLondon :
_bSpringer London,
_c2006.
300 _aL, 566 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aFundamentals of Machine Learning and Softcomputing -- Multilayer Perceptrons -- Hopfield Networks and Boltzmann Machines -- Competitive Learning and Clustering -- Radial Basis Function Networks -- Principal Component Analysis Networks -- Fuzzy Logic and Neurofuzzy Systems -- Evolutionary Algorithms and Evolving Neural Networks -- Discussion and Outlook.
520 _aConventional model-based data processing methods are computationally expensive and require experts’ knowledge for the modelling of a system; neural networks provide a model-free, adaptive, parallel-processing solution. Neural Networks in a Softcomputing Framework presents a thorough review of the most popular neural-network methods and their associated techniques. This concise but comprehensive textbook provides a powerful and universal paradigm for information processing. Each chapter provides state-of-the-art descriptions of the important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms, are introduced. These are powerful tools for neural-network learning. Array signal processing problems are discussed in order to illustrate the applications of each neural-network model. Neural Networks in a Softcomputing Framework is an ideal textbook for graduate students and researchers in this field because in addition to grasping the fundamentals, they can discover the most recent advances in each of the popular models. The systematic survey of each neural-network model and the exhaustive list of references will enable researchers and students to find suitable topics for future research. The important algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
650 0 _aEngineering.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition.
650 0 _aStatistical physics.
650 0 _aDynamical systems.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aStatistical Physics, Dynamical Systems and Complexity.
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aPattern Recognition.
700 1 _aSwamy, M. N. S.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781846283024
856 4 0 _uhttp://dx.doi.org/10.1007/1-84628-303-5
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c508652
_d508652