Principal Component Neural Networks : Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)
Автор:K. I. Diamantaras, S. Y. Kung Год: [не указано] Издание: [не указанo] Страниц: [не указано] ISBN: 0471054364 Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.
