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1)  operation decisionmaking algorithm
作战决策算法
2)  operations decision
作战决策
1.
Firstly, three problems are discussed: what effect every human thought way has in operations decision; how much capacity computers have on imitation for human thought; what limitations operations decision of highly automation has.
首先讨论了人的各种思维形式在作战决策中的作用、计算机模拟人的思维的能力和作战决策高度自动化的局限性。
3)  decision-making algorithm
决策算法
1.
Research on decision-making algorithm of offensive players’behavior in football match
足球比赛中进攻方队员行为决策算法研究
2.
A decision-making algorithm based on class algorithms is provided by which many ruleless and random data can be classified.
提出基于分类思想的性能判定决策算法,对大量的无规则的杂乱的网关经验数据进行分类。
3.
Aiming at this problem,multi-sensor data fusion algorithm and minimum risk decision-making algorithm are studied to meet the needs of HVAC system to make a reasonable decision-making.
针对HVAC(Heating Ventilation and Air Conditioning)系统检测装置和控制系统存在的时间滞后,使人体舒适性得不到很好的满足,并且有额外的能量消耗,进而研究了多传感器数据融合算法及基于D-S证据理论的最小风险决策算法以满足HVAC系统做出合理的决策。
4)  decision algorithm
决策算法
1.
An adaptation decision algorithm was also presented which was realized by retaining most of the higher quality layers, and dropping lower quality layers as far as possible.
264/AVC视频适配模型和系统,并设计了多媒体资源适配引擎,提出了相应的资源适配决策算法,即在满足网络可用带宽的约束条件下,丢弃低感知质量层的视频帧,而尽可能多地保留高感知质量层的视频帧。
2.
Then,according to the features of the generalized information table,the algorithms of acquiring minimal attribute reduction,attribute value reduction and minimum decision algorithm are put forward.
首先介绍了决策表、广义信息表的构造及特点,然后给出了求决策表的最小属性约简及最小决策算法的计算方法。
3.
It includes a behavior-based two-layer dynamic agent architecture, a behavior-based decision algorithm, and a role-based dynamic multi-agent cooperation model.
针对机器人足球仿真比赛,提出了一种新的多智能体系统结构,它包括基于行为的双层动态智能体结构和决策算法以及基于角色的动态多智能体协作模型。
5)  decision-making of aerial defence
防空作战决策
6)  combat decision-aid
作战辅助决策
1.
Based on the process of combat decision-aid, the article analyses the processing method of uncertain information.
根据作战辅助决策的过程,分别分析了对不确定性信息的处理方法。
补充资料:BP算法
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性质:又称逆推学习算法,简称BP算法,是1986年鲁梅哈特(D. E. Rumelhart)和麦克莱朗德(J. L. McClelland)提出来的。用样本数据训练人工神经网络(一种模仿人脑的信息处理系统),它自动地将实际输出值和期望值进行比较,得到误差信号,再根据误差信号从后(输出层)向前(输入层)逐层反传,调节各神经层神经元之间的连接权重,直至误差减至满足要求为止。反向传播算法的主要特征是中间层能对输出层反传过来的误差进行学习。这种算法不能保证训练期间实现全局误差最小,但可以实现局部误差最小。BP算法在图像处理、语音处理、优化等领域得到应用。

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