An efficient Symbiotic Taguchi-based Differential Evolution for neuro-fuzzy network design
Cheng‐Jian Lin, Chia-Hu Hsu, Siao-Yin Wu, Chun‐Cheng Peng · 2010
In this paper, we proposed a functional-link-based neural fuzzy network to improve the traditional TSK-type neural fuzzy network. Besides, an efficient evolutionary learning algorithm, called the Symbiotic Taguchi-based Modified Differential Evolution (STMDE), is proposed for the neural fuzzy networks design. Firstly, in order to avoid trapping in a local optimal solution and to ensure the searching capability of near global optimal solution, the STMDE adopts the Taguchi method to effectively search towards the best individual and employs an adaptive parameter control to adjust scaling factor which is called the Taguchi method. Moreover, the proposed STMDE introduces the concept of symbiotic evolution to improve the individual structure. Unlike the traditional individual that uses each one in a population as a full solution to a given problem, symbiotic evolution assumes that each individual in a population represents only a partial solution, while complex solutions combine several individuals in the population.