兵工自动化Issue(4)：88-92,5.DOI:10.7690/bgzdh.2016.04.023
一种改进粒子群算法优化BP神经网络实现核素识别方法
Optimize BP Neural Network by an Improved Particle Swarm Optimization to Implement Nuclide Identification
刘议聪 1朱泓光 1宋永强1
作者信息
- 1. 绵阳市维博电子有限责任公司,四川绵阳 621000
- 折叠
摘要
Abstract
To get the global optimal point, propose an optimize BP neural network by an improved particle swarm optimization (PSO) to implement nuclide identification. It changes inertia weight and learning factor dynamically with self-adaption to optimize the weight value and threshold value of BP neural network. It gets the global optimal value of the particle swarm by training BP neural network to identify models. Finally, it implements nuclide identification by using the optimal weight and threshold value. The experiment shows our proposed method can not only converge to the optimal value faster but also do a good balance between local search and global search. Therefore, it significantly improves the convergence speed and the accuracy of nuclide identification.关键词
粒子群算法/核素识别/全局最优点/惯性权重/学习因子Key words
PSO/nuclide identification/global optimal point/inertia weight/learning factor分类
信息技术与安全科学引用本文复制引用
刘议聪,朱泓光,宋永强..一种改进粒子群算法优化BP神经网络实现核素识别方法[J].兵工自动化,2016,(4):88-92,5.
