Intelligent Systems: Architectures and Perspectives
  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. Computational intelligence
is an innovative framework for constructing intelligent hybrid architectures
involving Neural Networks (NN), Fuzzy Inference Systems (FIS), Probabilistic
Reasoning (PR) and derivative free optimization techniques such as Evolutionary
Computation (EC). Most of these hybridization approaches, however, follow an ad
hoc design methodology, justified by success in certain application domains.
Due to the lack of a common framework it often remains difficult to compare the
various hybrid systems conceptually and to evaluate their performance
comparatively. This chapter introduces the different generic architectures for
integrating intelligent systems. The designing aspects and perspectives of
different hybrid archirectures like NN-FIS, EC-FIS, EC-NN, FIS-PR and NN-FIS-EC
systems are presented. Some conclusions are also provided towards the end.
