Comparison of hybrid neural systems of KSOM-BP learning in artificial odor recognition system

Abstract

This report proposes an adaptive recognition system, which is based on Kohonen self-organization network (KSOM). As the goals in the research on artificial neural network are to improve the recognition capability of the network and at the same time minimize the time needed for learning the patterns, these goals could be achieved by combining two types of learning, i.e. supervised learning and unsupervised learning. We have developed a new kind of hybrid neural learning system, combining unsupervised KSOM and supervised back-propagation learning rules. This hybrid neural system will henceforth be referred to as hybrid adaptive SOM with winning probability function and supervised BP or KSOM(WPF)-BP. This hybrid neural system could estimate the cluster distribution of given data, and directed it into predefined number of cluster neurons through creation and deletion mechanism. Comparison with other developed hybrid neural system is done for determination of various odors from Martha Tilaar Cosmetics product in an artificial odor recognition system. The performance of our developed learning system in term of its recognition ability and its learning time is explored in this report.