Development of a systematic methodology of fuzzy logic modeling

This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces four parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. Unlike traditional approach of selecting the inference mechanism a priori, the fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input–output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering algorithm. Major bottlenecks of the algorithm are addressed and analytical solutions are suggested. Furthermore, we also address the classification process in fuzzy modelng to extend the derived fuzzy partition to the entire output space. This issue remains unattained in the current literature. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy line clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples—a nonlinear function and a gas furnace dynamic procedure—are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches.