[Paper Review] A Study of an Modeling Method of T-S fuzzy System Based on Moving Fuzzy Reasoning and Its Application

Son-Il Kwak, Gang Choe+3 more|arXiv (Cornell University)|Nov 8, 2015

Fuzzy Logic and Control Systems34 references3 citations

TL;DR

This paper proposes a novel T-S fuzzy modeling method based on moving rate reasoning to enhance fuzzy identification effectiveness. By redefining membership function transitions (s-type, z-type, trapezoidal) using moving rates instead of traditional matching degrees, the method improves modeling accuracy in applications like precipitation forecasting and security situation prediction, with test results showing significant performance gains over existing approaches.

ABSTRACT

To improve the effectiveness of the fuzzy identification, a structure identification method based on moving rate is proposed for T-S fuzzy model. The proposed method is called "T-S modeling (or T-S fuzzy identification method) based on moving rate". First, to improve the shortcomings of existing fuzzy reasoning methods based on matching degree, the moving rates for s-type, z-type and trapezoidal membership functions of T-S fuzzy model were defined. Then, the differences between proposed moving rate and existing matching degree were explained. Next, the identification method based on moving rate is proposed for T-S model. Finally, the proposed identification method is applied to the fuzzy modeling for the precipitation forecast and security situation prediction. Test results show that the proposed method significantly improves the effectiveness of fuzzy identification.

Motivation & Objective

• To address limitations in existing fuzzy reasoning methods based on matching degree in T-S fuzzy modeling.
• To improve the effectiveness and accuracy of fuzzy identification in complex system modeling.
• To develop a new structure identification method for T-S fuzzy systems using moving rate-based reasoning.
• To validate the proposed method in real-world applications such as precipitation forecasting and security situation prediction.

Proposed method

• Defined moving rates for s-type, z-type, and trapezoidal membership functions in T-S fuzzy models to replace traditional matching degree concepts.
• Introduced a new fuzzy reasoning mechanism based on moving rate to better capture dynamic transitions in fuzzy sets.
• Formulated a structure identification method for T-S fuzzy models using the proposed moving rate framework.
• Applied the method to real-world data sets for precipitation forecasting and security situation prediction to evaluate performance.

Experimental results

Research questions

• RQ1 How can fuzzy reasoning in T-S fuzzy systems be improved beyond traditional matching degree-based methods?
• RQ2 What is the impact of using moving rates instead of matching degrees on the accuracy of T-S fuzzy model identification?
• RQ3 Can the proposed moving rate-based method effectively model complex, dynamic systems such as weather and security patterns?
• RQ4 How does the proposed method compare to existing T-S fuzzy identification techniques in real-world forecasting applications?

Key findings

• The proposed moving rate-based method significantly improves the effectiveness of fuzzy identification compared to conventional matching degree approaches.
• The method demonstrates enhanced modeling accuracy in precipitation forecasting, outperforming existing techniques.
• Security situation prediction using the proposed method shows improved reliability and responsiveness to dynamic changes.
• The use of moving rates for s-type, z-type, and trapezoidal membership functions enables more precise and adaptive fuzzy reasoning in T-S models.