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

Date

1999-08-04

Author

Sisman, NA
Alpaslan, Ferda Nur
Akman, V

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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.

Subject Keywords

Fuzzy neural networks, Autoregression, Temporal neural networks, Rule-based systems

URI

https://hdl.handle.net/11511/55545

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

N. Sisman, F. N. Alpaslan, and V. Akman, “A neuro-fuzzy MAR algorithm for temporal rule-based systems,” 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55545.