Integration of Neural Networks and Fuzzy Systems

The literature reveals that the ANN and Fuzzy set theoretic approaches have been often used for non-linear and complex problems such as load forecasting in power system. The integration of these approaches gives improved results as compared to conventional techniques. Both the modeling techniques have their own merits and demerits as follows:

• 1. Fuzzy models possess large power in representing linguistic and structured knowledge by fuzzy sets and performing fuzzy reasoning by fuzzy logic in qualitative manner and usually rely on the domain experts to provide the required knowledge for a specific problem. Further, the compensatory operators in the fuzzy models as connectives are found quite suitable and produce results, which are very close to the actual results (Mizumoto 1989).

• 2. On the other hand, neural network models are particularly good for non-linear mappings and for providing parallel processing facility to simulate complex system. The neural network models are developed via training.

• 3. Furthermore, while the behavior of fuzzy models can be understood easily due to their logical structure and step-by-step inference procedures. Neural network models act normally as a black box, without providing explanation facility.