To detect small moving targets in complex visible image background clutter, the spatial-temporal difference method is presented.

针对可见光图像背景下的运动小目标检测，提出了一种空时域联合差分的检测方法。

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A method combining temporal difference method with neural network model is applied to predict the sulfur content of the hot metal.

将时差方法与人工神经元网络方法有机地结合起来，提出了一种在高炉铁水含硫量预报中的应用策略。

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Then based on the temporal difference models, we formulate the detection problem in 2 steps, correlation detection and generalized likelihood ratio test (GLRT).

然后基于时域差分模型提出了红外慢速小目标时域检测算法，算法共分为两步：相关检测和广义似然比检测。

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Then the four main algorithms including dynamic programming, monte carlo method, temporal-difference and Q-learning are given respectively, and their difference and relation are pointed out.

动态规划、蒙特卡罗算法、时序差分算法、Q-学习，并指出了它们之间的区别和联系。

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Then the four main algorithms including dynamic programming, monte carlo method, temporal-difference and Q-learning are given respectively, and their difference and relation are pointed out.

动态规划、蒙特卡罗算法、时序差分算法、Q-学习，并指出了它们之间的区别和联系。

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