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Proceedings Paper

Fast training analog approximator on the basis of Legendre polynomials
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Paper Abstract

In a number of applications the approximation, interpolation or nonlinear extrapolation of certain weakly (when every subsequent term of power series expansion is much less than previous one) nonlinear dependencies d(x), where x an arbitrary signal in time, is demanded. The problem of cancellation of nonlinear distortions of a signal in high precision analog engineering can be an example. In such cases it seems to be reasonable to use polynomial-based devices. In this paper the neural network based devices able to perform the operations of approximation, interpolation and nonlinear extrapolation are described. The schemes and working characteristics of a breadboard, based on analog radio components, are presented. Legendre polynomials were offered as basis functions for significant increasing of the speed of the approximator training. The scheme of analog synthesizer of Legendre polynomials was also suggested.

Paper Details

Date Published: 4 April 1997
PDF: 6 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271501
Show Author Affiliations
Vyacheslav N. Chesnokov, Institute of Radio Engineering and Electronics (Russia)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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