Hybrid learning algorithm for Gaussian potential function networks

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Abstract
A new hybrid learning algorithm is proposed for use in the parametric estimation of Gaussian potential function networks (GPFNs). In the new algorithm, the number of network inputs is augmented by using target output values in the learning centres of Gaussian nodes in the network's hidden layer. This augmentation of input leads to a more reasonable distribution of centres in the hidden layer of a GPFN. A critical angle technique is then used to determine those nodes in which the shape factors will need further tuning by optimisation techniques. Two numerical examples are supplied to show the superior performance of this new algorithm as compared to that achieved through a traditional hybrid learning method, or to the optimised-only method of Lee and Kil. The capability of the GPFN as a dynamical model for continually tracking dynamics of non-stationary and time-varying systems is also illustrated.

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