{"product_id":"9781441949882","title":"Cellular Neural Networks: Analysis, Design and Optimization","description":"\u003ch1\u003eCellular Neural Networks: Analysis, Design and Optimization\u003c\/h1\u003e \u003ch2\u003eHänggi, Martin; Moschytz, George  S.\u003c\/h2\u003e \u003cp\u003eCellular 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. \u003cbr\u003e  Signal processing via CNNs only becomes efficient if the network is  implemented in analog hardware. In view of the physical limitations  that analog 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  analog 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 method is based  on a design centering technique which is generally applicable where  linear constraints have to be satisfied in an optimum way. \u003cbr\u003e  Processing speed is always crucial when discussing signal-processing  devices. In the case of the CNN, it is shown that the setting time can  be specified in closed analytical expressions, which permits, on the  one hand, parameter optimization with respect to speed and, on the  other hand, efficient numerical integration of CNNs. Interdependence  between robustness and speed issues are also addressed. Another goal  pursued is the unification of the theory of continuous-time and  discrete-time systems. By means of a delta-operator approach, it is  proven that the same network parameters can be used for both of these  classes, even if their nonlinear output functions differ. \u003cbr\u003e  More complex CNN optimization problems that cannot be solved  analytically necessitate resorting to numerical methods. Among these,  stochastic optimization techniques such as genetic algorithms prove  their usefulness, for example in image classificationproblems. Since  the inception of the CNN, the problem of finding the network  parameters for a desired task has been regarded as a learning or  training problem, and computationally expensive methods derived from  standard neural networks have been applied. Furthermore, numerous  useful parameter sets have been derived by intuition. \u003cbr\u003e  In this book, a direct and exact analytical design method for the  network parameters is presented. The approach yields solutions which  are optimum with respect to robustness, an aspect which is crucial for  successful implementation of the analog CNN hardware that has often  been neglected. \u003cbr\u003e  \u003cstrong\u003e`\u003c\/strong\u003eThis beautifully rounded work provides many interesting and  useful results, for both CNN theorists and circuit designers.\u003cstrong\u003e'\u003c\/strong\u003e  \u003cbr\u003e  \u003cstrong\u003eLeon O. Chua\u003c\/strong\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2010-10-29\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003e ISBN-10: 9781441949882\u003c\/p\u003e \u003cp\u003eISBN-13: 9781441949882\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4757-3220-7\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 148\u003c\/p\u003e ","brand":"Springer","offers":[{"title":"Default Title","offer_id":44358583877772,"sku":"9781441949882","price":99.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781441949882_e396441a-b556-4c29-a269-2109d213e1ce.jpg?v=1755109075","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781441949882","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}