An exact and direct analytical method for the design of optimally robust cnn templates

Martin Hänggi, George S. Moschytz

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

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 well-defined 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.

Original languageEnglish
Pages (from-to)304-311
Number of pages8
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume46
Issue number2
DOIs
StatePublished - 1999
Externally publishedYes

Keywords

  • Cellular neural networks (cnn's), robustness, template design

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