Matching Parameter Optimization in Self-Organizing Relationship (SOR) Network by Employing Energy Functions

− The self-organizing relationship (SOR) network was proposed in order to extract a desirable input-output relationship of a target system by using learning vectors with their evaluations. In the execution mode, the SOR network can be used as a fuzzy inference engine. The output of the SOR network depends on matching parameters which correspond to the standard deviation of the Gaussian membership function as 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 propose a method to optimize matching parameters of the SOR network. Energy functions are introduced to the SOR network in order to tune the matching parameter with a gradient descent method. The proposed method is verified through a function approximation problem.