Improved BP Neural Network Based on Simulated Annealing

ZHOU Ai-wu; ZHAI Zeng-hui; LIU Hui-ting

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Improved BP Neural Network Based on Simulated Annealing

更新时间：2026-09-04

- Improved BP Neural Network Based on Simulated Annealing
- Vol. 33, Issue 4, Pages: 144-147(2016)
- 作者机构：
  
  安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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周爱武, 翟增辉, 刘慧婷. 基于模拟退火算法改进的BP神经网络算法[J]. 微电子学与计算机, 2016, 33(4): 144-147.

ZHOU Ai-wu, ZHAI Zeng-hui, LIU Hui-ting. Improved BP Neural Network Based on Simulated Annealing[J]. 2016, 33(4): 144-147.
周爱武, 翟增辉, 刘慧婷. 基于模拟退火算法改进的BP神经网络算法[J]. 微电子学与计算机, 2016, 33(4): 144-147. DOI：

ZHOU Ai-wu, ZHAI Zeng-hui, LIU Hui-ting. Improved BP Neural Network Based on Simulated Annealing[J]. 2016, 33(4): 144-147. DOI：

摘要

提出一种基于模拟退火算法改进的BP神经网络.该方法利用模拟退火算法寻找更优化的样本子集

用来训练BP神经网络.通过理论分析以及实验仿真证明

在缩短训练时间以及迭代次数的基础上

显著提高BP神经网络的分类准确性.

Abstract

The BP neural network based on simulated annealing algorithm is given out in this paper

the method uses the simulated annealing algorithm to find a more optimal subset of samples

to train the BP neural network. Through theoretical analysis and experimental simulation proved that not only the time and the number of iterations is shorten

the accuracy of BP neural network is significantly improved.

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Keywords

references

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杨斌, 夏耀先. (2002). 基于改进共轭梯度法的前馈网络快速监督学习算法[J]. 电子学报, 2002, 30(12): 1845-1847. DOI: 1845

魏海坤. 神经网络结构设计的理论与方法[M]. 北京: 国防工业出版社, 2005.

康丽山. 非数值并行算法(第1册)——模拟退火算法[M]. 北京: 科学出版社, 1997: 22-55. DOI: 22

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胡静, 高隽, 杨静. 模糊神经网络分类器的主动学习方法[J]. 中国科学技术大学学报, 2008, 38(3): 241-246. DOI: 241

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