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.
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.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第4期144-147,共4页
Microelectronics & Computer
基金
国家自然科学基金项目(61202227)
关键词
BP神经网络
样本选择
主动学习
模拟退火
BP neural network
sample selection
active learning
simulated annealing
