000			04483nam a22005175i 4500
001			978-3-540-69226-3
003			DE-He213
005			20161121231201.0
007			cr nn 008mamaa
008			100301s2007 gw | s |||| 0|eng d
020			_a9783540692263 _9978-3-540-69226-3
024	7		_a10.1007/978-3-540-69226-3 _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		_aTang, Huajin. _eauthor.
245	1	0	_aNeural Networks: Computational Models and Applications _h[electronic resource] / _cby Huajin Tang, Kay Chen Tan, Zhang Yi.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2007.
300			_aXXII, 300 p. 103 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v53
505	0		_aFeedforward Neural Networks and Training Methods -- New Dynamical Optimal Learning for Linear Multilayer FNN -- Fundamentals of Dynamic Systems -- Various Computational Models and Applications -- Convergence Analysis of Discrete Time RNNs for Linear Variational Inequality Problem -- Parameter Settings of Hopfield Networks Applied to Traveling Salesman Problems -- Competitive Model for Combinatorial Optimization Problems -- Competitive Neural Networks for Image Segmentation -- Columnar Competitive Model for Solving Multi-Traveling Salesman Problem -- Improving Local Minima of Columnar Competitive Model for TSPs -- A New Algorithm for Finding the Shortest Paths Using PCNN -- Qualitative Analysis for Neural Networks with LT Transfer Functions -- Analysis of Cyclic Dynamics for Networks of Linear Threshold Neurons -- LT Network Dynamics and Analog Associative Memory -- Output Convergence Analysis for Delayed RNN with Time Varying Inputs -- Background Neural Networks with Uniform Firing Rate and Background Input.
520			_aNeural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain. Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily. Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.
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		_aTan, Kay Chen. _eauthor.
700	1		_aYi, Zhang. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783540692256
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v53
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-540-69226-3
912			_aZDB-2-ENG
950			_aEngineering (Springer-11647)
999			_c509789 _d509789