Fuzzy prediction architecture using recurrent neural networks

Authors:
Daniel Graves;Witold Pedrycz
Affiliations:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4
Venue:
Neurocomputing
Year:
2009

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Abstract

A fuzzy inference system (FIS) architecture based on the Takagi-Sugeno-Kang (TSK) fuzzy model is developed for time series prediction. Our objective is to investigate and evaluate the proposed rule-based model against commonly used time series models including ''standard'' architectures such as autoregressive (AR) models and selected topologies of neural networks. The main architectural developments of the FIS involve fuzzy relational antecedents (viz., antecedents represented in the form of fuzzy relations) and recurrent neural networks forming the consequents of the rules. Fuzzy C-means (FCM) clustering is applied to the time series to determine the fuzzy relations for the antecedents of the rules. Experimental results are reported for single-time step prediction and multiple time step (p-step) prediction on several widely used time series.