Particle swarm optimization algorithm of BP networks based on quantum excitation

REN Yu-yan1; BAO Jie2; WANG Hong-rui3

doi:10.13873/j.1000-97872011.07.048

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Particle swarm optimization algorithm of BP networks based on quantum excitation

更新时间：2025-09-11

- Particle swarm optimization algorithm of BP networks based on quantum excitation
- Vol. 30, Issue 7, Pages: 135-136+139(2011)
- 作者机构：
  
  1. 燕山大学里仁学院
  2. 国家发展和改革委员会国家投资项目评审中心
  3. 河北大学
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872011.07.048
  CLC： TP183
- Published：2011
- 稿件说明：
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[1]任玉艳,鲍洁,王洪瑞.基于量子激励的粒子群优化的BP网络算法[J].传感器与微系统,2011,30(07):135-136+139.

REN Yu-yan1, BAO Jie2, WANG Hong-rui3. Particle swarm optimization algorithm of BP networks based on quantum excitation[J]. 2011, 30(7): 135-136+139.
[1]任玉艳,鲍洁,王洪瑞.基于量子激励的粒子群优化的BP网络算法[J].传感器与微系统,2011,30(07):135-136+139. DOI： 10.13873/j.1000-97872011.07.048.

REN Yu-yan1, BAO Jie2, WANG Hong-rui3. Particle swarm optimization algorithm of BP networks based on quantum excitation[J]. 2011, 30(7): 135-136+139. DOI： 10.13873/j.1000-97872011.07.048.

摘要

提出一种基于量子激励粒子群算法优化BP网络参数的新方法。该算法在粒子群优化算法中引入量子论思想

克服了传统粒子群算法易陷入局部极值、优化效果较差的不足

最终得到BP网络的最佳参数值。将该算法应用于3个典型复杂函数

并与传统BP算法、基于传统的粒子群优化BP网络算法的仿真结果进行分析对比。结果表明:该算法训练次数少

模型精度高

性能优于其它两种BP网络算法。

Abstract

A new method to adjust weights of BP network is proposed based on quantum particle swarm optimization.This algorithm combines concepts of particle swarm and quantum principles

which overcome the limitations both the local extreme values and bad optimization result of traditional particle swarm optimization algorithm

and get the best-optimized parameters of BP network.The new algorithm is used in simulation of three typical complex functions

results of which are analyzed and compared with that of BP algorithm and BP network optimized based on traditional particle swarm optimization algorithm.Results show the performances of the new algorithm are superior to that of other two kinds of algorithms

especially in generalization ability.

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references

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改进粒子群—BP神经网络模型的短期电力负荷预测 [J]. 师彪,李郁侠,于新花,闫旺. 计算机应用 . 2009(04)

蚁群算法优化前向神经网络的一种方法 [J]. 王晶. 计算机工程与应用 . 2006(25)

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神经网络模型的预测精度影响因素分析及其优化 [J]. 陈果. 模式识别与人工智能 . 2005(05)

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