System identification by Cellular Neural Networks (CNN): linear interpolation of nonlinear weight functions

Michael Reinisch; Gunter Geis; Ronald Tetzlaff

doi:10.1117/12.608590

29 June 2005 System identification by Cellular Neural Networks (CNN): linear interpolation of nonlinear weight functions

Michael Reinisch, Gunter Geis, Ronald Tetzlaff

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Proceedings Volume 5839, Bioengineered and Bioinspired Systems II; (2005) https://doi.org/10.1117/12.608590
Event: Microtechnologies for the New Millennium 2005, 2005, Sevilla, Spain

Abstract

Recently CNN with nonlinear weight functions are used for various problems. Thereby nonlinear weights are represented by polynomials or tabulated functions combined with a cubic spline interpolation. In this paper a linear interpolation technique is considered to allow an accurate approximation of nonlinear weight functions in CNN. In a previous publication the Table Minimising Algorithm (TMA) was introduced and applied to the Korteweg-de Vries-equation (KdV). In this contribution new results obtained by applying the algorithm to additional partial differential equations (PDE) will be given and discussed.

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Michael Reinisch, Gunter Geis, and Ronald Tetzlaff "System identification by Cellular Neural Networks (CNN): linear interpolation of nonlinear weight functions", Proc. SPIE 5839, Bioengineered and Bioinspired Systems II, (29 June 2005); https://doi.org/10.1117/12.608590

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