29 June 2005 System identification by Cellular Neural Networks (CNN): linear interpolation of nonlinear weight functions
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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, 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); doi: 10.1117/12.608590; https://doi.org/10.1117/12.608590
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