A neuro-fuzzy MAR algorithm for temporal rule-based systems

Sisman N., Alpaslan F., Akman V.

World Multiconference on Systemics, Cybernetics and Informatics/5th International Conference on Information Systems, Analysis and Synthesis (ISAS 99), Florida, United States Of America, 31 July - 04 August 1999, pp.87-92 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Florida
  • Country: United States Of America
  • Page Numbers: pp.87-92
  • Middle East Technical University Affiliated: Yes


This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fuzzy linear functions with fuzzy number coefficients are used. The extracted rules are normally fed into a temporal fuzzy multilayer feedforward neural network. This method is applicable if obtaining fuzzy rules from crisp tabular data is required.