Hybrid Intelligent Systems: Evolving Intelligence in Hierarchical Layers

Hybridization of intelligent systems is a promising research field of modern computational intelligence concerned with the development of the next generation of intelligent systems. A fundamental stimulus to the investigations of hybrid intelligent systems is the awareness in the academic communities that combined approaches might be necessary if the remaining tough problems in artificial intelligence are to be solved. The integration of different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the hybridization or fusion of these techniques has, in recent years, contributed to a large number of new intelligent system designs. Most of these hybridization approaches, however, follow an ad hoc design methodology, justified by success in certain application domains. This chapter introduces the designing aspects and perspectives of two different hybrid intelligent system architectures involving fuzzy inference system, fuzzy clustering algorithm, neural network learning and evolutionary algorithm. The first architecture introduces a Takagi-Sugeno fuzzy inference system, which is optimized using a combination of neural network learning and evolutionary algorithm. In the second architecture a fuzzy clustering algorithm is used to segregate the data and then a fuzzy inference system is used for function approximation. Both architectures are validated using real world examples. Some conclusions are also provided towards the end.