工业工程2012，Vol.15Issue(4)：17-20,27,5.DOI:10.3969/j.issn.1007-7375.2012.04.004
基于粒子群BP神经网络的质量预测模型
Quality Prediction Model by Using PSO-BP Neural Network
摘要
Abstract
For quality assurance, it is very important to make effective quality prediction in the stage of product design and parameter optimization. To do so, by using PSO (particle swarm optimization) and BP (back propagation) neural network, a quality prediction model is established. It is an optimization problem with grey incidence degree between the network's output and input as objective. PSO algorithm is used to optimize the BP neural network's weight coefficient and threshold value. Then, a PSO-GRG ( grey relational grade) algorithm is proposed to solve the problem. This algorithm overcomes general BP algorithm's shortcomings of slow convergence and local optimum solution. A case problem of injection molding is used to verify the proposed method. Simulation results show that the prediction errors are significantly reduced with the number of iterations being reduced by 87. 5%.关键词
粒子群算法/BP神经网络/质量预测/灰色关联度Key words
particle swarm optimization (PSO) algorithm/ back propagation(BP) neural network/ quality prediction/ grey relational grade分类
管理科学引用本文复制引用
徐兰,方志耕,刘思峰..基于粒子群BP神经网络的质量预测模型[J].工业工程,2012,15(4):17-20,27,5.基金项目
国家自然科学青年基金资助项目(71002046) （71002046）
江苏省教育厅高校哲学社会科学研究基金资助项目(2012SJB630017) （2012SJB630017）
