改进PSO-BP算法在函数拟合中的应用  

The Application of Improved PSO-BP Algorithm in Nonlinear Function Approximating

作　　者：魏巍[1] 师娅[2] 

机构地区：[1]西安邮电大学物联网与两化融合研究院,陕西西安710061 [2]西安邮电大学通信与信息工程学院,陕西西安710121

出　　处：《微电子学与计算机》2017年第9期112-115,共4页Microelectronics & Computer

基　　金：陕西省教育厅科学研究计划资助项目(12JK0541)

摘　　要：提出一种基于粒子群的改进BP算法,该算法在网络的学习过程中首先利用粒子群算法的全局搜索性,引入非线性惯性系数找到最优权值,其次重新给部分粒子参数赋值,增加粒子多样性,从而避免早熟收敛,进一步完善了原有粒子群算法.建立非线性函数的BP神经网络模型,并利用MATLAB软件对其进行拟合.仿真结果表明,改进算法对于非线性函数有良好的拟合能力,拟合误差相对减小.An improved BP algorithm based on PSO is proposed. The algorithm in the network learning process using particle swarm algorithm global search, nonlinear inertial coefficient to find the optimal weights, then back to the part of the particle parameter assignment, so as to avoid premature convergence, further improve the original particle swarm optimization algorithm. The BP neural network model is constructed based on the dimensional nonlinear function The simulation results show that the improved algorithm has good fitting ability to two dimensional nonlinear function and fitting error is improved.

关 键 词：神经网络 BP算法 PSO算法 惯性权重 函数拟合 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]