Design of Information Granulation-Based Fuzzy Models with the Aid of Multi-objective Optimization and Successive Tuning Method

Abstract

In this paper, we propose a hybrid identification of information granulation-based fuzzy models by means of multi-objective optimization and successive tuning method. The proposed multi-objective algorithm using a nondominated sorting-based multi-objective strategy is associated with an analysis of solution space. The granulation of information is realized by means of the C-Means clustering algorithm. Information granules formed in this way become essential at further stages of the construction of the fuzzy models by forming the centers of the fuzzy sets constituting individual rules of the inference schemes. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification as well as parameter identification is simultaneously realized with the aid of successive tuning method. The evaluation of the performance of the proposed model was carried out by using two representative numerical examples such as NOx emission process data and Mackey-Glass time series. The proposed model is also contrasted with the quality of some “conventional” fuzzy models already encountered in the literature.