机械与电子2019，Vol.37Issue(1)：26-32,7.
基于BP神经网络和遗传算法的并行迭代优化研究
Research on Parallel Iterative Optimization Based on BP Neural Network and Genetic Algorithm
胡海涛1
作者信息
- 1. 烟台汽车工程职业学院, 山东 烟台 265500
- 折叠
摘要
Abstract
An iterative optimization method which combines back-propagation neural network (BPNN) with genetic algorithm (GA) was proposed.Firstly, the BPNN model was developed and trained with fewer learning samples, and then the trained BPNN model was solved by GA in the feasible region to find the optimal solution of the model.The validation result based on the optimal solution was added to the training pattern set as a new sample to retrain the BPNN model.Aiming at the problem that less training modes may lead to inadequate prediction accuracy, Bayesian regularization algorithm (BRA) and improved Levenberg-Marquardt algorithm were adopted to improve the generalization ability and convergence of BPNN model in training BPNN model, and elite strategy was combined to embed simulated annealing algorithm (SAA) into GA to improve the local search ability of the high BPNN model.The proposed method was applied to the thickness optimization of polypropylene bellows for automobile blow molding.The results show that the optimum mold clearance can be obtained after three iterations.The thickness of bellows with the optimum mold clearance at the nine tooth peaks falls within the expected range (0.7+0.05mm), and the material usage is reduced by 22％.The optimum clearance can be obtained by only 23experiments, which is far less than the number of experiments needed in the actual molding process.关键词
反向传播神经网络/遗传算法/并行优化/Levenberg-Marquardt算法/模拟退火算法/吹塑成型Key words
BPNN/GA/parallel optimization/Levenberg-Marquardt algorithm/SAA/blow molding分类
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胡海涛..基于BP神经网络和遗传算法的并行迭代优化研究[J].机械与电子,2019,37(1):26-32,7.
