{"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}
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.