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Perceptrons --- Congresses

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Detectors --- Perceptrons --- Information theory

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Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a"fault tolerance hint" can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant

Neural networks (Computer science) --- Perceptrons.

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Pattern perception --- Perceptrons --- Perception de structure

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Geometry --- Machine Learning --- Parallel processing (Electronic computers) --- Perceptrons