Evaluation-Based Topology Representing Network for Accurate Learning of Self-Organizing Relationship Network

Abstract

A Self-Organizing Relationship (SOR) network approximates a desirable input-output (I/O) relationship of a target system using I/O vector pairs and their evaluations. However, in the case where the topology of the network is different from that of the data set, the SOR network cannot precisely represent the topology of the data set and generate desirable outputs, because topology of the SOR network is fixed in one- or two dimensional surface during learning. On the other hand, a Topology Representing Network (TRN) precisely represents the topology of the data set by a graph using the Competitive Hebbian Learning. In this paper, we propose a novel method which represents topology of the data set with evaluation by creating a fusion of SOR network and TRN.