火力与指挥控制
火力與指揮控製
화력여지휘공제
FIRE CONTROL & COMMAND CONTROL
2009年
12期
166-170
,共5页
遗传算法%优化%BP算法%装备研制%风险分析
遺傳算法%優化%BP算法%裝備研製%風險分析
유전산법%우화%BP산법%장비연제%풍험분석
genetic algorithms%optimizing%back-propagation algorithms%weapon research and development%risk analysis
简要介绍了GA和BP算法,利用遗传算法全局性搜索的特点,改变BP算法依赖梯度信息的指导来调整网络权值的方法,寻找最为合适的网络连接权和网络结构,提出了遗传算法优化BP神经网络的思路及其数学模型.最后,结合对某型航空装备的风险源的分析,利用此优化模型进行了验证.结果表明了该方法的可行性,为装备研制风险分析提供了一种新思路.
簡要介紹瞭GA和BP算法,利用遺傳算法全跼性搜索的特點,改變BP算法依賴梯度信息的指導來調整網絡權值的方法,尋找最為閤適的網絡連接權和網絡結構,提齣瞭遺傳算法優化BP神經網絡的思路及其數學模型.最後,結閤對某型航空裝備的風險源的分析,利用此優化模型進行瞭驗證.結果錶明瞭該方法的可行性,為裝備研製風險分析提供瞭一種新思路.
간요개소료GA화BP산법,이용유전산법전국성수색적특점,개변BP산법의뢰제도신식적지도래조정망락권치적방법,심조최위합괄적망락련접권화망락결구,제출료유전산법우화BP신경망락적사로급기수학모형.최후,결합대모형항공장비적풍험원적분석,이용차우화모형진행료험증.결과표명료해방법적가행성,위장비연제풍험분석제공료일충신사로.
Genetic algorithms(GA) and back-propagation(BP) algorithms are introduced briefly. BP algorithm adjusts network weight according to the instruction of grads information. Using the globally search capability of GA to modify the method of looking for the most suitable network structure and link weight. Putting forward the way of BP optimized by GA. Bringing forward its mathematics model. Finally, verification of the model is carried on by the risk source analysis of the aviation equipment. Result expresses the possibility of this method which provides a new way for the risk analyzing of weapon research and development .
