兵工自动化
兵工自動化
병공자동화
ORDNANCE INDUSTRY AUTOMATION
2014年
9期
27-30
,共4页
武器装备%状态评估%主成分分析%遗传算法%BP神经网络
武器裝備%狀態評估%主成分分析%遺傳算法%BP神經網絡
무기장비%상태평고%주성분분석%유전산법%BP신경망락
weapon equipment%condition estimating%principle components analysis%genetic algorithm%BP neural network
针对传统评估方法主观性强的缺点及 BP 神经网络自身缺陷,提出基于数据知识的 PCA-GA-BP 状态评估组合算法。采用主成分分析对样本数据进行降维处理,利用遗传算法对 BP 神经网络的初始权值阈值进行优化,将历史数据作为学习样本训练神经网络,处理实时信息得到评估结果,并通过实例进行算法验证分析。结果表明,该算法是可行的,适用于复杂武器装备的状态评估。
針對傳統評估方法主觀性彊的缺點及 BP 神經網絡自身缺陷,提齣基于數據知識的 PCA-GA-BP 狀態評估組閤算法。採用主成分分析對樣本數據進行降維處理,利用遺傳算法對 BP 神經網絡的初始權值閾值進行優化,將歷史數據作為學習樣本訓練神經網絡,處理實時信息得到評估結果,併通過實例進行算法驗證分析。結果錶明,該算法是可行的,適用于複雜武器裝備的狀態評估。
침대전통평고방법주관성강적결점급 BP 신경망락자신결함,제출기우수거지식적 PCA-GA-BP 상태평고조합산법。채용주성분분석대양본수거진행강유처리,이용유전산법대 BP 신경망락적초시권치역치진행우화,장역사수거작위학습양본훈련신경망락,처리실시신식득도평고결과,병통과실례진행산법험증분석。결과표명,해산법시가행적,괄용우복잡무기장비적상태평고。
Aiming at the traditional evaluation methods has disadvantage of strong subjectivity and defects of BP neural network, the combinational algorithm PCA-GA-BP based on data is established. Sample data dimensions are reduced by principal component analysis, the initial weights and threshold of BP neural network are optimized by genetic algorithm. The neural network is trained by historical data and can be used to evaluate real-time information, and algorithm is validated through the case analysis. The results show that, the algorithm is feasible, which is suitable to condition evaluation for complex weapon equipment.