Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19] [ 20]


Journal of Information Science and Engineering, Vol. 23 No. 2, pp. 463-477 (March 2007)

Design of Neuro-Fuzzy Systems Using a Hybrid Evolutionary Learning Algorithm*

Cheng-Jian Lin and Yong-Ji Xu
Department of Computer Science and Information Engineering
Chaoyang University of Technology
Taichung, 413 Taiwan

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently using the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, with multiple groups in a population. Moreover, the proposed HELA method adopts the compact genetic algorithm (CGA) to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method performs better than some existing methods.

Keywords: evolutionary algorithms, TSK-type fuzzy model, variable-length genetic algorithm, identification, control

Full Text () Retrieve PDF document (200703_07.pdf)

Received February 21, 2005; revised April 27, 2005; accepted June 8, 2005.
Communicated by Chin-Teng Lin.
* This research was supported in part by the National Science Council of Taiwan, R.O.C., under grant No. NSC 95-2221-E-324-028.