Prediction of Time Sequence Using a TSK-Type Fuzzy Cerebellar Model Articulation Controller Network
Keywords:
TSK-type fuzzy model, cerebellar model articulation controller (CMAC), self-clustering, backpropagation, prediction.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.
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