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

Paper Details

Date Published: 29 June 2005
PDF: 6 pages
Proc. SPIE 5839, Bioengineered and Bioinspired Systems II, (29 June 2005); doi: 10.1117/12.608590
Show Author Affiliations
Michael Reinisch, Johann Wolfgang Goethe Univ. (Germany)
Gunter Geis, Johann Wolfgang Goethe Univ. (Germany)
Ronald Tetzlaff, Johann Wolfgang Goethe Univ. (Germany)

Published in SPIE Proceedings Vol. 5839:
Bioengineered and Bioinspired Systems II
Ricardo A. Carmona; Gustavo Linan-Cembrano, Editor(s)

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