KCMAC-TSK: A fuzzy cerebellar model with localized TSK learning for non-linear system identification

Many real-world systems exhibit complex dynamic nonlinear characteristics that cannot be modeled by typical statistical and machine learning models. The human cerebellum is a vital part of the brain system that possesses the capability to accurately model highly nonlinear physical dynamics. We can exploit our increasing knowledge of the human cerebellum to construct an intelligent computational model to effectively handle the complexity of nonlinear dynamic systems in the real world. This paper presents a novel brain-inspired computational model of the human cerebellum named the kernel density-based CMAC with Takagi-Sugeno-Kang fuzzy inference model (KCMAC-TSK) for fast and accurate nonlinear system identification. The structure of the KCMAC-TSK model is inspired by the neurophysiological aspects of cerebellar learning and development process. By incorporating a fuzzy model in KCMAC-TSK using kernel density estimation, we enhance the modeling capability, accuracy, and interpretability of the system. We applied the proposed KCMAC-TSK model in a challenging highway traffic flow modeling and prediction problem. Experimental results showed that KCMAC-TSK outperformed current modeling techniques, demonstrating the learning accuracy and effectiveness of KCMAC-TSK in handling complex nonlinear dynamic real-world systems.