A Novel Interval Type-2 Fuzzy System Identification Method Based on the Modified Fuzzy C-Regression Model
In this article, a novel interval type-2 Takagi-Sugeno fuzzy c-regression modeling method with a modified distance definition is proposed.
Technology Overview
Utilizing a modified distance definition, the real shortest distance from the data points to the type-2 fuzzy hyperplane function is calculated to determine the proper antecedent and consequent parameters. A modified objective function with the modified distance definition is developed to improve the robustness of the proposed identification method.
Applications & Benefits
In future investigation, based on the proposed identification method, the affections for the rule number, fuzzy weighting exponents m1, m2, and the decay rate γ will be explored to improve the proposed results. Furthermore, the proposed identification method will also be applied to different real models, such as an automobile city-cycle fuel consumption in miles per gallon (Auto MPG) or an aerobic reactor.
Abstract：
In this article, a novel interval type-2 Takagi-Sugeno fuzzy c-regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to describe the distance between each data point and the local type-2 fuzzy model. To improve the robustness of the proposed identification method, a modified objective function is presented. In addition, different from most previous studies that require numerous free parameters to be determined, an interval type-2 fuzzy c-regression model is developed to reduce the number of such free parameters. Furthermore, an improved ratio between the upper and lower weights is proposed based on the upper and lower membership function with each input data, and the ordinary least-squares method is adopted to establish the type-2 fuzzy model. The Box-Jenkins model and two numerical models are given to illustrate the effectiveness and robustness of the proposed results.
A Novel Interval Type-2 Fuzzy System Identification Method Based on the Modified Fuzzy C-Regression Model
Author：Tsai S., Chen Y.
Year：2022
Source publication：IEEE Transactions on Cybernetics , Volume: 52, Issue: 9
Subfield Highest percentage：99% Control and Systems Engineering #1/260