Neuro-Fuzzy Hybrid Intelligent System Using Grid Computing

Abstract

Hybrid intelligent systems are generally complex in nature. One of the hybrid intelligent systems is neuro- fuzzy hybrid intelligent system. The neuro-fuzzy systems are transparent systems which are capable of learning as well. Takagi Sugeno N-F system, a specific type of N-F system is a better performer than other N-F systems, but it needs more computational time. Besides that N-F system retains the property of parallel processing that it inherits from neural networks. To that extent, there appears to be much in common with the field of grid computing which itself aims to support applications that are parallel in nature and need high computational power. In this paper we examine the similarities between these fields and the potential offered by grid computing to solve computational problems of N-F systems by proposing a method for developing N-F systems using grid computing.