Fuzzy prediction architecture using recurrent neural networks

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.

• Publication year

  2009

• Venue

  Neurocomputing

• Publication date

  2009-03-01

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1016/j.neucom.2008.07.009
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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