A temporal neuro-fuzzy approach for time-series analysis

Date

2003-09-08

Author

Şişman Yılmaz, Arzu
Alpaslan, Ferda Nur

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In this paper, a temporal neuro-fuzzy system is presented which provides an environment that keeps temporal rela tionships between input and output variables. The sys tem is used to forecast the future behavior of time series data. It is based on ANFIS neuro-fuzzy system and named ANFIS unfolded in time. The rule base contains tempo ral TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, a modified back-propagation learning algorithm is used. The model is tested on Gas-furnace data which is a benchmark problem.

Subject Keywords

Neuro-fuzzy system, Unfolding-in-time

URI

https://hdl.handle.net/11511/87604

Conference Name

https://www.actapress.com/Abstract.aspx?paperId=15081

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Şişman Yılmaz and F. N. Alpaslan, “A temporal neuro-fuzzy approach for time-series analysis,” Benalmadena, Spain, 2003, p. 679, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87604.