An Energy Function-Based Optimization of Matching Parameters and Reference Vectors in SOR Network

Abstract

In this paper we propose an energy function-based optimization method in order to improve the approximation ability of the self-organizing relationship (SOR) network. In the execution mode, the SOR network can be used as a fuzzy inference engine. The output of the SOR network is calculated by using the reference vectors and matching parameters. The matching parameters, which correspond to the standard deviation of the Gaussian membership function used in fuzzy inference, are only defined in the execution mode. However, the issue of the optimization of the matching parameters has not yet been treated in previous works. To optimize the matching parameters, we introduce an energy function to the SOR network. The energy function can be used to tune not only the matching parameters but also the reference vectors with a gradient descent method. The proposed method is applied to a function approximation problem and the improvement of the approximation ability is confirmed.