重庆理工大学学报（自然科学版）2013，Vol.27Issue(1)：37-41,5.DOI:10.3969/j.issn.1674-8425(z).2013.01.008
基于粒子群的BP神经网络算法在猪等级评定中的应用
Study on Pork Grade Evaluation of BP Neural Network Based on PSO
摘要
Abstract
The BP neural network algorithm has been widely applied to the pork grade evaluation. But because of the defect in BP neural network of pork grade evaluation at present which is sensitive with the initial weights, easy to fall into the local least value, low forecast precision and slow convergence speed occurred. This paper introduced the particle swarm optimization (PSO) algorithm based on the random global optimization into the neural network training. First the PSO is used to optimize weights of BP neural network, and then neural network is used for given accuracy to found the PSO-BP neural network model. Compared with the traditional BP neural network, the PSO-BP neural network model has the merits of faster convergence speed and higher forecast precision, and it can be effectively applied to the pork grade evaluation.关键词
猪等级评定/粒子群算法/BP神经网络Key words
pork grade evaluation/ particle swarm optimization algorithm/ BP neural network分类
计算机与自动化引用本文复制引用
王越,曾晶,董丽梅,张权..基于粒子群的BP神经网络算法在猪等级评定中的应用[J].重庆理工大学学报（自然科学版）,2013,27(1):37-41,5.基金项目
重庆市科技攻关项目(2010GGB097) （2010GGB097）
