Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm

XIE Su-chao(谢素超)¹, ZHOU Hui(周辉)², ZHAO Jun-jie(赵俊杰)¹, ZHANG Yi-cheng(章易程)¹

(1. Key Laboratory of Traffic Safety on Track of Ministry of Education
(School of Traffic & Transportation Engineering, Central South University), Changsha 410075, China;
2. School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract:In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by unifing respective applicability of back-propagation artificial neural network (BP-ANN) and genetic algorithm (GA). The detailed process was as follows. firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting th, e desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. the average relative forecast error (A_RE) of the S_EA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the A_RE of the S_EA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.

Key words:thin-walled structure; GA-BP hybrid algorithm; impact; energy-absorption characteristic; forecast