Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation

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This text presents a method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other alternative algorithms for hardware-implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analogue hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed-weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips.