New Training Methods for RBF Neural Networks

Radial basis function neural networks have been proposed as powerful tools in many applications. Existing training 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 parameter initializing, pre-training and post-training methods to reach better capabilities in learning time and desired precision compared to previous RBF networks