首页 | 本学科首页   官方微博 | 高级检索  
     

基于改进BP网络的装甲装备机动性能评估模型
引用本文:徐波,刘建永,李凌,智德.基于改进BP网络的装甲装备机动性能评估模型[J].兵工自动化,2009,28(6):92-93.
作者姓名:徐波  刘建永  李凌  智德
作者单位:解放军理工大学,工程兵工程学院,江苏,南京,210007
摘    要:收敛速度慢、易陷入局部极值是传统的BP神经网络难以避免的问题,最终可能导致网络训练失败.在量化装甲装备机动性能指标的基础上,采用遗传算法对BP神经网络权值进行优化,用自适应梯度下降法对传统BP神经网络进行训练,从而建立装甲装备机动性能评估模型,并通过二次训练得到评估值.仿真结果表明该改进网络收敛速度明显优于传统网络,能有效避免局部板值问题。

关 键 词:装甲装备机动性能  遗传算法  BP神经网络  二次训练

Evaluation Model for Flexibility Effectiveness of Armored Weapon System Based on Improved BP Neural Network
XU Bo,LIU Jian-yong,LI Ling,ZHI De.Evaluation Model for Flexibility Effectiveness of Armored Weapon System Based on Improved BP Neural Network[J].Ordnance Industry Automation,2009,28(6):92-93.
Authors:XU Bo  LIU Jian-yong  LI Ling  ZHI De
Affiliation:Engineering College of Engineering Corps;PLA University of Science & Technology;Nanjing 210007;China
Abstract:inevitable problems in traditional BP neural networks like slow convergence and easy in local extremum may finally lead to abortive net training. On the basis of quantifying the index of armored equipment's flexibility performance, we ,optimized the weight of BP neural network by using genetic algorithm; trained traditional BP neural network through adaptive gradient descent algorithm, and established evaluation model for flexibility performance of armored equipment; while trained network to obtain evaluation value. Simulation results show the convergent speed of the improved network is much better than that of traditional neural network, and can effectively avoid local extremum.
Keywords:Flexibility effectiveness of armored weapon system  Genetic algorithm  BP neural network  Secondary training  
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号