New enhanced methods for radial basis function neural networks in function approximation

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机构：
[1] Fatemi, Mehdi
[2] Roopaei, Mehdi
[3] Shabaninia, Faridoon
来源：
Fatemi, M. (mefatemi@gmail.com) | / Operador Nacional do Sistema Eletrico - ONS; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior卷 / Inst. of Elec. and Elec. Eng. Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States期
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Theoretical; (THR);
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摘要：
Function Approximation is a widely used method in System Identification and recently RBF networks have been proposed as powerful tools for that. Existing algorithms suffer from some restrictions such as slow convergence and/or encountering to bias in parameter convergence. This paper is an attempt to improve the above problems by proposing new methods of parameter initializing and post-training to reach better capabilities in learning time and desired precision compared to previous RBF networks. © 2005 IEEE.
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