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

Sisman, NA
Alpaslan, Ferda Nur
Akman, V
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.


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Yılmaz (Şişman), Nuran Arzu; Alpaslan, Ferda Nur; Department of Computer Engineering (2003)
The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may ...
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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.