000			04217nam a2200553 i 4500
001			6267393
003			IEEE
005			20220712204651.0
006			m o d
007			cr |n|||||||||
008			151223s1993 maua ob 001 eng d
020			_z9780262527897 _qprint
020			_a0262071452
020			_a9780262071451
020			_a9780262273404 _qebook
020			_z0585040281 _qelectronic
020			_z9780585040288 _qelectronic
020			_z0262273403 _qelectronic
035			_a(CaBNVSL)mat06267393
035			_a(IDAMS)0b000064818b43bd
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.87 _b.G35 1993eb
100	1		_aGallant, Stephen I., _eauthor. _922553
245	1	0	_aNeural network learning and expert systems / _cStephen I. Gallant.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1993.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1993]
300			_a1 PDF (xvi, 365 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
500			_a"A Bradford Book."
504			_aIncludes bibliographical references (p. [349]-359) and index.
505	0		_a1. Introduction and important definitions -- 2. Representation issues -- 3. Perceptron learning and the pocket algorithm -- 4. Winner-take-all groups or linear machines -- 5. Autoassociators and one-shot learning --
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aMost neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus.The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more.In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN.The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs.Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aExpert systems (Computer science) _93392
650		0	_aNeural networks (Computer science) _93414
650		7	_aCOMPUTERS _xEnterprise Applications _xBusiness Intelligence Tools. _2bisacsh _922116
650		7	_aCOMPUTERS _xIntelligence (AI) & Semantics. _2bisacsh _922117
655		0	_aElectronic books. _93294
710	2		_aIEEE Xplore (Online Service), _edistributor. _922554
710	2		_aMIT Press, _epublisher. _922555
776	0	8	_iPrint version _z9780262527897
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267393
942			_cEBK
999			_c73047 _d73047