000			01746cam a2200301 a 4500
001			1086096
005			20200126093752.0
008			920615s1994 maua b 001 0 eng
010			_a92002345
020			_a020156629X
020			_a9780201566291
035			_a(Sirsi) u996
035			_9(DLC) 92002345
040			_aEG-CaNU _cEG-CaNU _dEG-CaNU
042			_ancode
050	0	0	_aQA76.87 _b .F72 1994
082	0	0	_a006.3 _2 20
100	1		_aFreeman, James A. _917639
245	1	0	_aSimulating neural networks with Mathematica / _c James A. Freeman.
260			_aReading, Mass. : _b Addison-Wesley, _c c1994.
300			_ax, 341 p. : _b ill. ; _c 25 cm.
504			_aIncludes bibliographical references (p. 335-336) and index.
505	0		_aIntroduction to Neural Networks and Mathematica. -- Training by Error Minimization. -- Backpropagation and Its Variants. -- Probability and Neural Networks. -- Optimization and Constraint Satisfaction with Neural Networks. -- Feedback and Recurrent Networks. -- Adaptive Resonance Theory. -- Genetic Algorithms.
520			_aThis book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. Also for researchers and practitioners interested in using Mathematica as a research tool.Features
630	0	0	_aCIT. _914
650		0	_aNeural networks (Computer science) _91507
596			_a1
999			_c8690 _d8690