Principal Component Neural Networks : Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) K. I. Diamantaras, S. Y. Kung

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. Это и многое другое вы найдете в книге Principal Component Neural Networks : Theory and Applications (Adaptive and Learning Systems for Signal Processing, Communications and Control Series) (K. I. Diamantaras, S. Y. Kung)