This paper proposes a simplified method on fuzzy identification algorithm. The main purpose of this method is to reduce computational quantity for identifying a fuzzy model. In particular, the procedure for finding an optimal structure of fuzzy partition is simplified. This algorithm consists of four stages which realize structure identification and parameter identification of a fuzzy model. The Widrow-Hoff learning algorithm, which is a learning method of neural networks, is used for parameter identification of a fuzzy model.
The aim of the first stage is to identify a linear model. The second stage finds an optimal structure of fuzzy partition which is defined by fuzzy sets in premise parts. The third stage realizes structure identification in consequent parts of a fuzzy model which has an optimal structure of fuzzy partition determined in the second stage. The last stage achieves parameter identification of a final fuzzy model.
The procedure of this algorithm is concretely demonstrated by a simple example, which has been used in some modeling exercises. Finally, this algorithm is applied to modeling of a municipal refuse incinerator. Identification results show that this algorithm is very useful for modeling of complex systems such as the municipal refuse incinerator.