Intelligent Fusion and Transformation Systems

In recent times, research efforts have been directed towards hybridization of various intelligent methodologies in order to solve complex industrial problems. At the same time these research efforts have been undertaken to develop a better understanding of the human information processing system. Today, one can find a number of applications involving hybridization of intelligent methodologies like knowledge based systems, fuzzy systems, genetic algorithms, and case based reasoning with artificial neural networks. The central methodology of many hybrid systems has been artificial neural networks. In fact, 2/3rd of the applications involving intelligent hybrid systems use neural networks. Broadly, in these applications neural networks are either used as the primary problem solving entity, or are used in conjunction with other intelligent methodology/ies which have a distinct and separate role to play in the problem solving process. The former are categorized as fusion and/or transformation based approaches, and the later are categorized as combination approaches. However, fusion and/or transformation based approaches center around not only neural networks but also center around the other other methodology, namely, genetic algorithms. Similarly, the combination approaches can involve intelligent methodologies other than neural networks also. The goal of this chapter is to provide an overview to the reader about fusion and transformation based synergies with neural network and genetic algorithms as the primary problem solving entities. In this direction, this chapter looks at the neuro-symbolic systems, neuro-fuzzy systems, genetic-neuro systems, and genetic-fuzzy systems.

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