计算机工程与科学2012，Vol.34Issue(2)：146-149,4.DOI:10.3969/j.issn.1007-130X.2012.02.027
基于粒子群优化的灰色神经网络组合预测模型研究
A PSO-Based Combined Forecasting Grey Neural Network Model
摘要
Abstract
Gray neural network in the field of artificial intelligence prediction has been applied widely, but it has such problems as the slow speed of convergence, and local minimum, so its forecast preci sion is limited partly. This paper, in view of its defects, proposes the learning algorithm of the BP neural network optimized by PSOCParticle swarm algorithm). On the basis of this algorithm, grey prediction is used to make a preliminary forecast for the stock index futures' historical data, and the results of initial forecasts are used as the input of the optimized BP neural network to be forecast and trained. A PSO-based Combined forecasting Grey Neural Network model(PSO-GMNN) is built. Finally, the simu lation experiment result indicates that the prediction accuracy of the new prediction model is higher than that of the BP neural network, the gray neural network and the gray prediction model. It also shows the effectiveness and feasibility of the method.关键词
BP神经网络/粒子群算法/灰色预测/灰色神经网络/PSO-GMNNKey words
BP neural network/particle swarm optimization/grey/grey neural network/PSO-GMNN分类
信息技术与安全科学引用本文复制引用
马吉明,徐忠仁,王秉政..基于粒子群优化的灰色神经网络组合预测模型研究[J].计算机工程与科学,2012,34(2):146-149,4.基金项目
河南省科技攻关项目(092102210108) （092102210108）
