NDLI: Cellular neural networks with nearly arbitrary nonlinear weight functions

Content Provider	IEEE Xplore Digital Library
Author	Loncar, A. Tetzlaff, R.
Copyright Year	2000
Description	Author affiliation: Inst. fur Angewandte Phys., Frankfurt Univ., Germany (Loncar, A.)
Abstract	We present cellular neural networks (CNN) with a new type of nonlinear weight functions. Instead of representing a weight function by a n-th order polynom, we propose tabulated functions by using a cubic spline interpolation procedure. These CNN are considered for the problem of modelling nonlinear systems, which are characterized by partial differential equations (PDE). Therefore we propose a training algorithm to adjust the behaviour of CNN solutions to the solutions of a given nonlinear system. Results are given for the /spl Phi//sup 4/-equation and the achieved accuracy is compared to the approximation accuracy of solutions obtained by a direct spatial discretization of the /spl Phi//sup 4/-equation.
Starting Page	171
Ending Page	176
File Size	268297
Page Count	6
File Format	PDF
ISBN	0780363442
DOI	10.1109/CNNA.2000.876840
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-05-25
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cellular neural networks Nonlinear systems Spline Nonlinear dynamical systems Differential equations Polynomials Interpolation Electronic mail Partial differential equations Nonlinear equations
Content Type	Text
Resource Type	Article

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Cellular neural networks with nearly arbitrary nonlinear weight functions