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.
引用本文: 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]. Microelectronics & Computer, 2016, 33(4): 144-147.
Citation: ZHOU Ai-wu, ZHAI Zeng-hui, LIU Hui-ting. Improved BP Neural Network Based on Simulated Annealing[J]. Microelectronics & Computer, 2016, 33(4): 144-147.

基于模拟退火算法改进的BP神经网络算法

Improved BP Neural Network Based on Simulated Annealing

  • 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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