| Title |
Design of HCBKA-Based TSK Fuzzy Prediction System with Error Compensation |
| Authors |
방영근(Bang, Young-Keun) ; 이철희(Lee, Chul-Heui) |
| Keywords |
TSK Fuzzy System ; HCBKA ; Difference Data ; Error Compensation ; Time Series |
| Abstract |
To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples. |