Improvements and critique on Sugeno's and Yasukawa's qualitative modeling

This paper investigates Sugeno's and Yasukawa's qualitative fuzzy modeling approach. We propose some easily implementable solutions for the nuclear details of the original paper, such as trapezoid approximation of membership functions, rule creation from sample data points, and selection of important variables. We further suggest an improved parameter identification algorithm to be applied instead of the original one. These details are crucial concerning the method's performance as it is shown in comparative analysis and helps to improve the accuracy of the built-up model. Finally, we propose a possible further rule base reduction which can be applied successfully in certain cases. This improvement reduces the time requirement of the method by up to 16% in our experiments.