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

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2 Author(s)
M. Hanggi ; Signal & Inf. Process. Lab., Fed. Inst. of Technol., Zurich, Switzerland ; G. S. Moschytz

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

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IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications  (Volume:46 ,  Issue: 2 )