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Fifth International Conference on Hybrid Intelligent Systems (HIS'05)
New Enhanced Methods for Radial Basis Function Neural Networks in Function Approximation
Rio de Janeiro, Brazil
December 06-December 09
ISBN: 0-7695-2457-5
Mehdi Fatemi, Shiraz University
Mehdi Roopaei, Shiraz University
Faridoon Shabaninia, Shiraz University
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.
Citation:
Mehdi Fatemi, Mehdi Roopaei, Faridoon Shabaninia, "New Enhanced Methods for Radial Basis Function Neural Networks in Function Approximation," his, pp.524-527, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005
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