Identification of fuzzy models using a successive tuning method with a variant identification ratio

The paper is concerned with the identification of fuzzy inference systems (fuzzy models) realized with the aid of the successive tuning method using a variant identification ratio that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFC-PGA) and information granulation. The HFC-PGA is a certain multi-population version of Parallel Genetic Algorithms (PGA), which is suitable for a simultaneous optimization of both the structure of the fuzzy model as well as its parameters. The granulation of information is realized with the aid 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 (modal values) of the fuzzy sets constituting individual rules of the inference schemes. Further optimization of the fuzzy model deals with an adjustment of a suite of parameters (such as the number of input variables to be used in the model, a collection of specific subsets of the input variables, and the number of membership functions) being used by these variables, and the order and parameters of the polynomial occurring in the conclusions of the corresponding rules. An iterative development of the fuzzy model deals with its structural as well as parametric optimization via HFC-PGA, the C-Means algorithm, and a standard least square estimation method. To evaluate the performance of the proposed model, we exploit well known and commonly used data sets such as gas furnace, Mackey-Glass time series as well as medical imaging system. A comparative analysis demonstrates that the proposed model leads to the superior performance when compared with other fuzzy models reported in the literature.