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

WEI Wei; SHI Ya

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

更新时间：2026-09-04

- The Application of Improved PSO-BP Algorithm in Nonlinear Function Approximating
- Vol. 34, Issue 9, Pages: 112-115(2017)
- 作者机构：
  
  1. 西安邮电大学机构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
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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魏巍, 师娅. 改进PSO-BP算法在函数拟合中的应用[J]. 微电子学与计算机, 2017, 34(9): 112-115.

WEI Wei, SHI Ya. The Application of Improved PSO-BP Algorithm in Nonlinear Function Approximating[J]. 2017, 34(9): 112-115.
魏巍, 师娅. 改进PSO-BP算法在函数拟合中的应用[J]. 微电子学与计算机, 2017, 34(9): 112-115. DOI：

WEI Wei, SHI Ya. The Application of Improved PSO-BP Algorithm in Nonlinear Function Approximating[J]. 2017, 34(9): 112-115. DOI：

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.

Abstract

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.

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references

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田雨波, 潘朋朋. 混合粒子群算法优化神经网络的研究[J]. 微电子学与计算机, 2011, 28(6): 156-159. DOI: 156

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