Rational Function Functional Networks Based on Function Approximation

Abstract:

In order to solve function approximation, a mathematic model of Rational Function Functional Networks (RFFN) based on approximation was proposed and the learning algorithm for function approximation was presented. This algorithm used the lease square method thought and constructed auxiliary function by Lagrange multiplier method, and the parameters of the rational function functional networks were determined by solving a system of linear equations. Results illustrate the effectiveness of the rational function functional networks in solving approximation problems of the function with a pole.

• Periodical: Applied Mechanics and Materials (Volumes 220-223)
• Pages: 2264-2268
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.220-223.2264
• Online since: November 2012
• Authors: Dong Dong Wang, You Jun Chen, Hai Jie Pang
• Keywords: Basis Functions, Function Approximation, Functional Networks, Lagrange Multiplier Method, Learning Algorithm, Rational Function

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