Parametric identification for nonlinear response model of a stranded wire helical spring based on improved reverse learning difference evolution Algorithm

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (1) : 187-194.

DING Chuanjun, ZHANG Xiangyan, LIU Ning

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Abstract

Aiming at shortcomings of traditional algorithms in identifying the response model parameters of a stranded wire helical spring, an improved reverse difference evolution algorithm was proposed.A novel reverse learning strategy was used to guide populations approaching the global optimal solution.In order to improve populations’ diversity, the sinusoidal chaotic sequence was used to calculate the scaling factor.These two mechanisms cooperatively operated to better balance the global exploration ability and the local mining one of the proposed algorithm.Through establishing the objective function of parametric identification and conducting dynamic tests of a stranded wire helical spring, the improved algorithm was used to optimize the objective function, and obtain the model parameters.The results showed that the proposed algorithm can effectively recognize the response model parameters of the stranded wire helical spring; the parameters’ convergence speed and the success rate of computation are superior to those of the standard reverse difference evolution algorithm and other algorithms; even in the case of higher noise level, the proposed algorithm can correctly recognize model parameters of stranded wire helical springs.

Key words

stranded wire helical spring / parameter identification / nonlinear hysteresis model / differential evolution algorithm / opposition-based learning / sinusoidal chaotic sequence

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DING Chuanjun, ZHANG Xiangyan, LIU Ning. Parametric identification for nonlinear response model of a stranded wire helical spring based on improved reverse learning difference evolution Algorithm[J]. Journal of Vibration and Shock, 2019, 38(1): 187-194

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