Neural Networks : A comprehensive Foundation / Simon Haykin - New Jersey : Prentice Hall, 1994 - xvi, 696 p.: il.;, 25 cm.
Apéndices p. 617 Incluye abreviaciones y símbolos Problemas al finald de cada capítulo
What 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.