Computational Intelligence: Applications of Fuzzy Logic

In fuzzy modelling, the identification method used is very important. Identification for fuzzy modelling has two aspects: structure identification and parameter identification. In general, structure identification is a difficult and extremely ill-defined process and not readily amenable to automated techniques. The problem of parameter identification is closely related to the estimation of the membership functions of the fuzzy sets or alternatively, the fuzzy relation associated with the fuzzy model. Generally structure identification constitutes two problems. To find input/output variables from a number of input/output candidates by a heuristic method based on experience and/or common sense knowledge. To find input-output relations in the form of if-then rules. In a fuzzy model, the structure identification of this kind is stated in a different way. A fuzzy model consists of a number of if-then rules. The number of rules, n, in a fuzzy model corresponds to the order of the model in a conventional method. There are two parts in an if-then rule (see Figure 1): antecedent part and consequent part.