000			01491cam a22002897a 4500
001			004460
003			armpuni
005			20160923153929.0
008			160923s1994####xx#a##########000#0#und#d
040			_aarmpuni _carmpuni
041			_aen
080			_a519.7
100	1		_aHaykin, Simon
245	1	0	_aNeural Networks : _bA comprehensive Foundation / _cSimon Haykin
260			_aNew Jersey : _bPrentice Hall, _c1994
300			_axvi, 696 p.: _bil.;, 25 cm.
500			_aApéndices p. 617
500			_aIncluye abreviaciones y símbolos
500			_aProblemas al finald de cada capítulo
550			_aWhat is a neural network?. Learning process. Correlation matrix memory. The perceptron. Least-Mean-Square algorithm. Multilayer perceptrons. Back-propagation and differentiation. Radial-Basis Function networks. Recurrent networks rooted in statistical physics. Self-Organizing systems I: Hebbian learning. Self-organizing systems II: Competitive learning. Self-organizing systems III: Information-theoretic models. Modular networks. Temporal processing. Neurodynamics. VLSI Iplementations of neural networks. Pseudoinverse matrix memory. A general tool for convergence. Analysis of stochastic. Approximation algorithms. Statical thermodynamics. Fokker-plank equation.
650		7	_aAUTOORGANIZACION _2LEMB
650		7	_aINTELIGENCIA ARTIFICIAL _2LEMB
650		7	_aNEURAL NETWARKS _2LEMB
650		7	_aREDES NEURONALES _2LEMB
942			_cLB _2cdu
945			_aMDC _d1999-09-14
999			_c4459 _d5634