Cheng Yi-Chang, Hsu Yung-Chi, Lin Sheng-Fuu
Department of Electrical and Control Engineering, National Chiao-Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.
Expert Syst Appl. 2010 Jul 1;37(7):5320-5330. doi: 10.1016/j.eswa.2010.01.003.
In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations.
本文提出了一种基于多组合作共生进化方法的TSK型神经模糊系统（TNFS-MGCSE）。TNFS-MGCSE是从共生进化发展而来的。共生进化不同于传统的遗传算法，在共生进化中，每个染色体代表一个模糊模型规则。MGCSE与传统的共生进化不同；在MGCSE中，一个种群被划分为几个组。由一组染色体组成的每个组代表一个模糊规则，并与其他组合作，通过所提出的基于合作的交叉策略（CCS）生成更好的染色体。本文通过数值例子（Mackey-Glass混沌时间序列和太阳黑子数预测）对所提出的TNFS-MGCSE进行评估。在模拟中，TNFS-MGCSE的性能与其他现有模型相比表现出色。
