Learning of SOR network employing soft-max adaptation rule of neural gas network

A Self-Organizing Relationship Network (SORN) can approximate the desirable input/output (I/O) relationship of a target system from not only good examples but also bad ones. The learning of SORN is achieved with employing the soft-max adaptation rule of Self-Organizing Maps (SOM). In this paper, we simplify the learning law by employing the soft-max adaptation rule of Neural Gas Network. This modification improves the approximation performances and lightens burdens imposed on a network designer in the design process of SORN.