Tuning of Matching Parameters and Reference Vectors in Self-Organizing Relationship (SOR) Network by Employing an Energy Function

Abstract

The self-organizing relationship (SOR) network was proposed in order to extract a desirable input-output relationship of a target system by using input-output data pairs with their evaluations. 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 reference vectors and matching parameters, which correspond to the standard deviation of the Gaussian membership function used in fuzzy inference. However, the issue of the optimization of the matching parameters has not yet been treated in previous works. In this paper, we introduce an energy function to the SOR network in order to tune the matching parameters. The energy function can be used not only to tune the matching parameters but also to fine-tune 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.