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An Exact and Direct Analytical Method for the Design of Optimally Robust CNN Templates
 IEEE TRANS. CIRCUITS & SYST.I
, 1999
"... In this paper, we present an analytical design approach for the class of bipolar cellular neural networks (CNN's) which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all welldefined CNN tasks are characterized by a finit ..."
Abstract

Cited by 5 (2 self)
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In this paper, we present an analytical design approach for the class of bipolar cellular neural networks (CNN's) which yields optimally robust template parameters. We give a rigorous definition of absolute and relative robustness and show that all welldefined CNN tasks are characterized by a finite set of linear and homogeneous inequalities. This system of inequalities can be analytically solved for the most robust template by simple matrix algebra. For the relative robustness of a task, a theoretical upper bound exists and is easily derived, whereas the absolute robustness can be arbitrarily increased by template scaling. A series of examples demonstrates the simplicity and broad applicability of the proposed method.
Optimization of CNN Template Robustness
, 1999
"... Introduction 1.1 The Classo Bip Cellular Neural Netwo0A In this letter, weco00b the classo singlelayer, spatially invariant cellular neural netwo05 (CNNs) with neighbogho d radiusodi foiu wing thedefinitio given in [1]. The dynamicso the netwo isgo verned by a systemo MN di#erentialequatio5b ..."
Abstract
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Introduction 1.1 The Classo Bip Cellular Neural Netwo0A In this letter, weco00b the classo singlelayer, spatially invariant cellular neural netwo05 (CNNs) with neighbogho d radiusodi foiu wing thedefinitio given in [1]. The dynamicso the netwo isgo verned by a systemo MN di#erentialequatio5b dx i (t) dt = x i (t)+ # k#N i # a k f(x k (t)) + b k u k # + I (1) where N idenob the neighoig o d o the cell C i , A = {a k } and B = {b k } the feed ack and co tro template parameters, respectively. f() is