Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference metho...Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.展开更多
针对当前考虑弹药消耗多重影响因素所反映的规律不够客观,长时间弹药消耗没有考虑其消耗规律等问题,提出了利用长短期记忆神经网络(long short term memory,LSTM)来分析弹药消耗的规律。通过示例数据的训练集和测试集,来进行弹药消耗的...针对当前考虑弹药消耗多重影响因素所反映的规律不够客观,长时间弹药消耗没有考虑其消耗规律等问题,提出了利用长短期记忆神经网络(long short term memory,LSTM)来分析弹药消耗的规律。通过示例数据的训练集和测试集,来进行弹药消耗的预测。通过对比RNN模型和BP神经网络模型在测试集上的平均绝对误差(mean absolute error,MAE)和均根方差(root mean square error,RMSE),LSTM神经网络在MAE和RMSE上的误差小,对于长时间序列弹药消耗数据有着很好的预测效果。展开更多
基金the Army Scientific Research(KYSZJWJK1744,012016012600B11403).
文摘Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.
文摘针对当前考虑弹药消耗多重影响因素所反映的规律不够客观,长时间弹药消耗没有考虑其消耗规律等问题,提出了利用长短期记忆神经网络(long short term memory,LSTM)来分析弹药消耗的规律。通过示例数据的训练集和测试集,来进行弹药消耗的预测。通过对比RNN模型和BP神经网络模型在测试集上的平均绝对误差(mean absolute error,MAE)和均根方差(root mean square error,RMSE),LSTM神经网络在MAE和RMSE上的误差小,对于长时间序列弹药消耗数据有着很好的预测效果。
