石油科技成果经济效益评价方法研究

依据石油科技成果的类型、专业类别、经济效益体现形式,计算其经济效益便可形成成千上万个计算公式,这给成果经济效益计算带来较大困难,为此提出了计算石油科技成果经济效益的增量思想、黑箱思想和分解叠加思想,并建立了石油科技成果经济效益计算指标体系,包括4个统计型和4个预测型效益指标,探讨了计算其经济效益的通用数学公式。以及经济效益预测的新模型。经过应用检验。说明文中提出的方法完全可以解决应用型石油科技成果经济效益的计算问题。     

多子带雷达信号融合噪声抑制方法

全极点模型对信号构建线性模型,通过对模型进行定阶和计算可以得到估计信号的各个参数,从而实现信号预测。子带融合之前需要进行相干处理,可以通过相干函数等方式解决。root-MUSIC算法在低信噪比环境中极点选择并不稳健,会导致模型在阶数上判断错误。为解决全极点模型方法中子带融合在信噪比低时模型阶数估计不准的问题,提出一种噪声抑制方法。对信号Hankel矩阵的主对角奇异值矩阵加权处理,以消除噪声分量,并利用整体前向预测矩阵得到整体频段极点值与极点幅值,由此估计出低信噪比时的多子带融合信号。结果表明所提方法在仿真环境下,在信噪比-20 dB至10 dB时,相较于传统极点模型方案具有更好的估计结果。     

基于可能性决策的敌对危机行动预测与仿真

可能性决策能较好地应对军事领域常常出现的概率难以获得,或者强调“出奇制胜”而有意规避概率风险的情形,是解决知识不完备情况下敌对行动预测的有力手段。从可能性理论基本公理体系出发,结合动态规划方法,提出并证明可能性决策的最优化定理,给出多步条件下可能性决策的实现算法,结合危机条件下敌对行动预测的范例和仿真分析,与传统概率风险决策进行比较,体现可能性决策的优越性,为该方法的推广应用创造条件。     

基于神经网络的弹药消耗预测

针对弹药消耗预测问题,运用一种改进的BP神经网络预测方法。预测时对样本数据进行了预处理,并在时序训练样本中引入了遗忘因子,以提高当前预测的精度,以及在权值调整过程中,引入"惯性项",以改善学习收敛过程。     

Commitment decisions with partial information updating

In this paper, we extend the results of Ferguson M. Naval Research Logistics 8 . on an end‐product manufacturer's choice of when to commit to an order quantity from its parts supplier. During the supplier's lead‐time, information arrives about end‐product demand. This information reduces some of the forecast uncertainty. While the supplier must choose its production quantity of parts based on the original forecast, the manufacturer can wait to place its order from the supplier after observing the information update. We find that a manufacturer is sometimes better off with a contract requiring an early commitment to its order quantity, before the supplier commits resources. On the other hand, the supplier sometimes prefers a delayed commitment. The preferences depend upon the amount of demand uncertainty resolved by the information as well as which member of the supply chain sets the exchange price. We also show conditions where demand information updating is detrimental to both the manufacturer and the supplier. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

基于案例的推理在舰船维修费预测中的应用

针对市场经济条件下舰船维修费预测不确定性越来越大的问题,提出了一种基于案例推理的舰船维修费预测模型。首先,利用修船厂的历史数据,采用特征向量表示法构建舰船维修费案例库;其次,为提高案例检索效率,采用组合模糊聚类算法建立舰船维修费案例的分类索引结构,并提出基于最近邻法的两步检索策略检索相似案例;最后,采用加权平均法对相似案例进行修改以获取预测值,同时采用主动学习策略保存当前案例并更新案例库。实例仿真结果表明:与传统舰船维修费预测模型相比,基于案例的推理模型具有更高的预测精度,所提模型是有效的。     

营房室内有害气体污染预测研究

由于营房建筑、装饰材料质量不合格,施工工艺不正确等原因,引起营房室内有害气体污染,严重威胁到住用者的生命健康和部队战斗力。研究营房室内有害气体污染变化发展的规律,对营房室内有害气体污染进行准确的预测,做好预防措施,可以避免营房室内有害气体污染的发生。采用灰色预测方法对营房室内有害气体污染水平进行预测,在 GM(1,1)模型的基础上进行改进,从而建立起营房室内有害气体污染水平预测模型。通过对预测模型进行检验,证实营房室内有害气体污染预测模型能较好的预测营房室内有害气体污染水平的发展变化趋势,模型的建立是成功的。     

基于马尔可夫分析的人才拥有量研究

基于马尔可夫分析理论,给出了人才拥有量预测的马尔可夫模型,并且讨论了模型中转移概率的确定及模型的预测质量等问题.     

When to commit in a serial supply chain with forecast updating

Negotiations between an end product manufacturer and a parts supplier often revolve around two main issues: the supplier's price and the length of time the manufacturer is contractually held to its order quantity, commonly termed the “commitment time frame.” Because actual demand is unknown, the specification of the commitment time frame determines how the demand risk is shared among the members of the supply chain. Casual observation indicates that most manufacturers prefer to delay commitments as long as possible while suppliers prefer early commitments. In this paper, we investigate whether these goals are always in the firm's best interest. In particular, we find that the manufacturer may sometimes be better off with a contract that requires an early commitment to its order quantity, before the supplier commits resources and the supplier may sometimes be better off with a delayed commitment. We also find that the preferred commitment time frame depends upon which member of the supply chain has the power to set their exchange price. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

基于系统关联分析的装备维修费用的灰色系统预测研究

利用系统分析的方法对影响装备维修费用的因素做了分析．使用灰色关联分析的方法讨论了影响装备维修费用的主要因素．通过灰色ＧＭ（１,１）建模,给出了装备维修费用的预测．