An online Bayesian Ying–Yang learning applied to fuzzy CMAC

Abstract

This paper proposes an online Bayesian Ying–Yang (OBYY) clustering algorithm, which is then applied to the fuzzy cerebellar model articulation controller (FCMAC). Inspired by ancient Chinese Ying–Yang philosophy, Xu's Bayesian Ying–Yang (BYY) learning has been successfully applied to clustering by harmonizing the visible input data (Yang) and the invisible clusters (Ying). In this research, the original BYY is advanced to dynamically recruit, adjust, and merge the fuzzy clusters to achieve maximum harmony and highest membership values. The proposed online FCMAC-BYY offers the following advantages. First, the antecedent of the fuzzy rules are dynamically constructed and optimized by the OBYY algorithm during the operation of the system. Second, the credit assignment is then employed in the learning process of the neurons to greatly speed up the learning process. These features make the entire online FCMAC-BYY an optimal structure with a fast learning speed that can perform online learning and suitable for real-time applications. The experimental results on some benchmark datasets show that the proposed model outperforms the existing representative techniques.