PSO优化BP神经网络的混沌时间序列预测  

Prediction for chaotic time series of optimized BP neural network based on PSO

作　　者：卢辉斌[1,2] 李丹丹[1,2] 孙海艳[3] 

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004 [2]河北省特种光纤与光纤传感实验室,河北秦皇岛066004 [3]河北建材职业技术学院机电工程系,河北秦皇岛066004

出　　处：《计算机工程与应用》2015年第2期224-229,264,共7页Computer Engineering and Applications

基　　金：河北省教育厅2007年科研计划项目(No.2007493)

摘　　要：针对于BP神经网络预测模型,收敛速度慢,精度较低,容易陷入局部极小值等缺点,提出了一种改进粒子群优化BP神经网络预测模型的算法。在该算法中,粒子群采用改进自适应惯性权重和改进自适应加速因子优化BP神经网络预测模型的初始权值和阈值,然后训练BP神经网络预测模型并预测。将该算法应用到几个典型的混沌时间序列预测。实验结果表明,该算法明显提高BP神经网络预测模型的收敛速度和预测模型的精度,减少陷入局部极小的可能。BP neural network for forecasting has low speed of convergence, low precision and easily falling into the local minimum state. An improved prediction method of optimized BP neural network based on Improved Particle Swarm Optimization algorithm（IPSO）is proposed. The IPSO algorithm adopts modified adaptive inertia weight and adaptive acceleration coefficients to optimize the weights and thresholds of BP neural network. Then BP neural network is trained to search for the optimal solution. This experiment is done with several typical nonlinear systems. The results demonstrate that the improved method has faster convergence speed, higher accuracy and not easily falling into the local minimum state.

关 键 词：混沌时间序列 混沌预测 反向传播(BP)神经网络 粒子群算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]