基于遗传算法和BP神经网络的武器系统安全评价模型

Cheng Kai, Zhang Hong-jun, Xuebing Bo, Shan Li-li
{"title":"基于遗传算法和BP神经网络的武器系统安全评价模型","authors":"Cheng Kai, Zhang Hong-jun, Xuebing Bo, Shan Li-li","doi":"10.1109/ICIST.2011.5765108","DOIUrl":null,"url":null,"abstract":"The traditional neural network is unavoidable to present local extreme value question, may result in failing training. On the basis of quantization of weapon system safe index, it has adopted neural network based on improved genetic algorithm to set up the systematic safety evaluation model of the weapon. It utilizes improved genetic algorithm to optimize the weight of neural network and get the final assessment value through twice training of neural network. The simulation result implies that the convergence speed of hybrid algorithm is quick and it can avoid local extreme value question effectively.","PeriodicalId":6408,"journal":{"name":"2009 International Conference on Environmental Science and Information Application Technology","volume":"64 1","pages":"830-833"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Weapon systematic safety evaluation model based on genetic algorithm and BP neural network\",\"authors\":\"Cheng Kai, Zhang Hong-jun, Xuebing Bo, Shan Li-li\",\"doi\":\"10.1109/ICIST.2011.5765108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The traditional neural network is unavoidable to present local extreme value question, may result in failing training. On the basis of quantization of weapon system safe index, it has adopted neural network based on improved genetic algorithm to set up the systematic safety evaluation model of the weapon. It utilizes improved genetic algorithm to optimize the weight of neural network and get the final assessment value through twice training of neural network. The simulation result implies that the convergence speed of hybrid algorithm is quick and it can avoid local extreme value question effectively.\",\"PeriodicalId\":6408,\"journal\":{\"name\":\"2009 International Conference on Environmental Science and Information Application Technology\",\"volume\":\"64 1\",\"pages\":\"830-833\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Environmental Science and Information Application Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIST.2011.5765108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Environmental Science and Information Application Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2011.5765108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

传统的神经网络不可避免地存在局部极值问题,可能导致训练失败。在量化武器系统安全指标的基础上,采用基于改进遗传算法的神经网络建立了武器系统安全评价模型。利用改进的遗传算法对神经网络权值进行优化,并通过对神经网络的两次训练得到最终的评价值。仿真结果表明,混合算法收敛速度快,能有效避免局部极值问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Weapon systematic safety evaluation model based on genetic algorithm and BP neural network
The traditional neural network is unavoidable to present local extreme value question, may result in failing training. On the basis of quantization of weapon system safe index, it has adopted neural network based on improved genetic algorithm to set up the systematic safety evaluation model of the weapon. It utilizes improved genetic algorithm to optimize the weight of neural network and get the final assessment value through twice training of neural network. The simulation result implies that the convergence speed of hybrid algorithm is quick and it can avoid local extreme value question effectively.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Applications of oriental systems methodology to system shortest path programming visualization research process ZigBee based wireless sensor networks for service robot intelligent space An adaptive control of Web QoS based on online identification Extended (G′ over G)-expansion method for Relativistic Toda Lattice system The study of Localization algorithm based on RSSI
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1