Abstract— This paper shows fundamentals and applications of the novel TSK-type fuzzy cerebellar model articulation controller (TSK-FCMAC) network. It resembles a neural structure that derived from the Albus CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. A self-constructing learning algorithm consists of the self-clustering method (SCM) and the backpropagation algorithm (BP) uses to tune the adjustable parameters are proposed. The SCM is a fast, one-pass algorithm for a dynamic estimation of the number of hypercube cells in an input data space. The clustering technique does not require prior knowledge of things such as the number of clusters present in a data set. The backpropagation algorithm is used to tune the adjustable parameters. Experimental results show the performance and applicability of the proposed model.
Index Terms— TSK-type fuzzy model, cerebellar model articulation controller (CMAC), self-clustering, backpropagation, prediction.
C. L. Lee is with the International Trade Department, National Taichung University of Science and Technology, Taichung City, Taiwan 404, ROC (e-mail: merrylee@nutc.edu.tw).
C. J. Lin is with the Computer Science and Information Engineering Department, National Chin-Yi University of Technology, Taichung City, Taiwan 411, ROC (e-mail: cjlin@ncut.edu.tw).
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Cite: Chin-Ling Lee and Cheng-Jian Lin, " Prediction of Time Sequence Using a TSK-Type Fuzzy Cerebellar Model Articulation Controller Network," International Journal of Information and Electronics Engineering vol. 4, no. 6, pp. 446-449, 2014.
