兵工自动化
兵工自動化
병공자동화
Ordnance Industry Automation
2015年
9期
26-28
,共3页
数据融合%BP神经网络%DS证据理论
數據融閤%BP神經網絡%DS證據理論
수거융합%BP신경망락%DS증거이론
data fusion%BP neural network%DS evidence theory
为了提高发射场试验信息处理结果的可靠性，减小冲突信息的影响，通过对发射场试验信息特点的分析及对其中关键技术的研究，提出融合BP神经网络与DS证据理论的信息处理方案。基于神经网络的输出结果建立证据理论的识别框架，通过证据理论相关合成法则对得到的生成融合决策，提高发射场设备状态评估与决策的准确性。
為瞭提高髮射場試驗信息處理結果的可靠性，減小遲突信息的影響，通過對髮射場試驗信息特點的分析及對其中關鍵技術的研究，提齣融閤BP神經網絡與DS證據理論的信息處理方案。基于神經網絡的輸齣結果建立證據理論的識彆框架，通過證據理論相關閤成法則對得到的生成融閤決策，提高髮射場設備狀態評估與決策的準確性。
위료제고발사장시험신식처리결과적가고성，감소충돌신식적영향，통과대발사장시험신식특점적분석급대기중관건기술적연구，제출융합BP신경망락여DS증거이론적신식처리방안。기우신경망락적수출결과건립증거이론적식별광가，통과증거이론상관합성법칙대득도적생성융합결책，제고발사장설비상태평고여결책적준학성。
Based on the analysis on characteristics of launching sites’ testing information and the study on the key technology involved, this paper proposes an information solution incorporated with BP neural network and DS evidence theory in order to increase the reliability of launching sites’ testing information results as well as decrease the impact of conflict information. Moreover, this paper builds a frame of discernment of evidence theory according to the output of neural networks. With related combination rules of evidence theory, it also decides on obtained combination so as to improve the accuracy of assessment and decision upon launching sites’ device status.
