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
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.80
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 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||