Nonlinear mappings with cellular neural networks

Authors:
J. Álvaro Fernández-Muñoz;Víctor M. Preciado-Díaz;Miguel A. Jaramillo-Morán
Affiliations:
Dpto. Electrónica e Ing. Electromecánica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, Spain;Laboratory for Electromagnetic and Electronic Systems (LEES), Massachusetts Institute of Technology, Cambridge, MA;Dpto. Electrónica e Ing. Electromecánica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, Spain
Venue:
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Year:
2005

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Abstract

In this paper, a general technique for automatically defining multilayer Cellular Neural Networks to perform Chebyshev optimal piecewise linear approximations of nonlinear functions is proposed. First, a novel CNN cell output function is proposed. Its main goal is to control input and output dynamic ranges. Afterwards, this 2-layer CNN is further programmed to achieve generic piecewise Chevyshev polynomials that approximate a nonlinear function.