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 performance enhancement.Contents:IntroductionNeural Network Performance MetricsNoise in Neural ImplementationsSimulation Requirements and EnvironmentFault ToleranceGeneralisation AbilityLearning Trajectory and SpeedPenalty Terms for Fault ToleranceConclusionsFault Tolerance Hints — The General CaseBibliographyIndexReadership: Scientists and researchers in neural networks and electrical & electronic engineering.Key Features:Innovative spectral and symplectic methods for integrability analysis of nonlinear dynamical systems on functional manifolds such as the new Delsarte-Lions/de Rham-Hodge characteristic classes — some of which have been developed by the authors — appear here for the first time in any bookThese new methods, which are illustrated by numerous examples and applications, are shown to be especially well-suited to studying higher dimensional non-isospectrally integrable systems, and those with non-local featuresThe new material is seamlessly embedded in an exposition that ranges from the basic elements of nonlinear dynamics to the leading-edge of current research, making the treatment largely self-contained and accessible to advanced students and researchers interested in dynamical integrability in mathematical, but having only a solid background in the fundamentals of modern dynamical systems theory and its applications to physics