JRM Vol.18 No.5 pp. 672-679
doi: 10.20965/jrm.2006.p0672


Time-Series Forecasting Using Fuzzy-Neural System with Evolutionary Rule Base

Arit Thammano* and Sirinda Palahan**

*Computational Intelligence Laboratory, Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand

**School of Science, University of the Thai Chamber of Commerce, 126/1 Vibhavadee-Rangsit Road, Dindaeng, Bangkok 10400, Thailand

February 3, 2006
July 19, 2006
October 20, 2006
time-series forecasting, fuzzy system, artificial neural network, genetic algorithm, hybrid system

This paper proposes a new hybrid time-series forecasting system which is the fusion of fuzzy systems and artificial neural networks. The proposed fuzzy-neural system consists of 5 layers: an input layer, fuzzification layer, rule layer, hidden layer, and output layer. The artificial neural network is used as the fuzzy inference engine and the genetic algorithm is used to optimize the fuzzy rule base. This proposed system was tested with 20 time-series datasets. The results obtained were very encouraging.

Cite this article as:
Arit Thammano and Sirinda Palahan, “Time-Series Forecasting Using Fuzzy-Neural System with Evolutionary Rule Base,” J. Robot. Mechatron., Vol.18, No.5, pp. 672-679, 2006.
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Last updated on Mar. 05, 2021