Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation Online publication date: Tue, 21-Oct-2014
by Chunshien Li, Tai-Wei Chiang
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 5, No. 4, 2011
Abstract: A new neuro-fuzzy computing paradigm using complex fuzzy sets to the problem of function approximation is proposed in this paper. The concept of complex fuzzy sets is an extension of traditional fuzzy set whose membership degrees are within a unit disc in the complex plane. The proposed complex system has excellent input-output mapping ability. To update the free parameters of the proposed complex neuro-fuzzy system (CNFS), a novel hybrid learning method is devised, combining both the well-known particle swarm optimisation (PSO) algorithm and the recursive least squares estimator (RLSE) algorithm. By the PSO-RLSE hybrid learning method, fast learning convergence is observed and better performance in accuracy is shown. To test the proposed approach, two benchmark functions are used. The experimental results by the proposed approach are compared to its neuro-fuzzy counterpart and to other approaches in literature. According to the experiment results, excellent performance by the proposed approach has been exposed.
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