Fuzzy identification by means of partitions of fuzzy input space and an aggregate objective function

In order to optimize fuzzy modeling of nonlinear system, we proposed a optimal fuzzy model according to the characteristic of I/O relationship, hard c-mean method, genetic algorithm, and objective function with weighting factor. A conventional fuzzy model has difficulty in definition of membership function. In order to solve its problem, the premise structure of the proposed fuzzy model is selected by both the partition of input space and the analysis of input-output relationship using the clustering algorithm. The premise parameters of the fuzzy model are optimized respectively by the genetic algorithm and the consequence parameters of the fuzzy model are identified by the standard least square method. Also, an aggregate objective function with weighting factor is proposed to achieve a balance between the performance results for the training and testing data.