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OverviewCellular Neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required. Signal processing via CNNs only becomes efficient if the network is implemented in analogue hardware. In view of the physical limitations that analogue implementations entail, robust operation of a CNN chip with respect to parameter variations has to be insured. By far not all mathematically possible CNN tasks can be carried out reliably on an analogue chip; some of them are inherently too sensitive. This book defines a robustness measure to quantify the degree of robustness and proposes an exact and direct analytical design method for the synthesis of optimally robust network parameters. The approach yields solutions which are optimum with respect to robustness, an aspect which is crucial for successful implementation of the analogue CNN hardware that has often been neglected. Full Product DetailsAuthor: Martin Hänggi , George S. MoschytzPublisher: Springer Imprint: Springer Edition: 2000 ed. Dimensions: Width: 15.50cm , Height: 1.10cm , Length: 23.50cm Weight: 0.910kg ISBN: 9780792378914ISBN 10: 0792378911 Pages: 148 Publication Date: 31 August 2000 Audience: College/higher education , Professional and scholarly , Undergraduate , Postgraduate, Research & Scholarly Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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