Fusion of Fuzzy Logic and Neural Networks with Applications to Decision and Control Problems

Human logic and inference mechanism have challenged mathematicians and cyberneticians for a long time. Recent developments in fuzzy logic and neural networks provide a new mathematical platform to develop new methodologies for the emulation of human-like interference mechanisms for its application to decision and control problems. In this paper, we provide a mathematical fusion of fuzzy logic and neural network for the development of fuzzy neural networks. Fuzzy neural networks provide a new methodology for handling a stream of qualitative (fuzzy) data with learning and adaptive capabilities, the important attributes of human cognition and perception. We expect that during the next deade, this new methology will lead to some innovative theoretical developments with extensive applications in the problems related to decision and control, expert systems, knowledge-based systems, pattern recognition, and emulation of problems related to human cognition and perception. This new theory will, hopefully, lead to the development of robust intelligent systems.