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发表于 2012-11-28 23:36:24
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上面提到了马尔可夫过程,这里就顺便再说一下随机因素在数学物理问题中的作用。以前在科学网何毓琦的博客中灌水时我曾作过如下的评论:
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On finding an ideal partner for life
http://blog.sciencenet.cn/home.php?mod=space&uid=1565&do=blog&id=276944
http://blog.yeshj.com/coolboy/archive/2009/12/26/1511545.html
Professor Ho mentioned Kalman filter in his replies to my above comments. In terms of the relative importance of determinant versus randomness, we probably can divide the stochastic optimization problems into two categories: (1) those where the determinate processes are dominant with small modifications coming from randomness and (2) those we know little about precise laws governing their changes due to randomness that makes the major contributions to system variations. One well-known example in the first category is the change and control of the state of a spacecraft (or any flying object) where stochastic uncertainties only slightly modify the known dynamical system. In this category, the Kalman filter is always the best method/approach to solve the stochastic optimization problems BECAUSE the Kalman filter fully utilizes the available knowledge/resource of the determinate dynamical system. On the other hand, if one likes to optimize a network of traffic flows by appropriately setting the timings of the traffic lights at all the interactions there is hardly any determinate law that will tell us, as a first order approximation, how the flow will evolve with time. In this case, the gradient needs to be evaluated entirely by the stochastic approximations.
An introductory essay on Kalman filter is given below:
A physicist’s view on Kalman filter [coolboy]
http://blog.hjenglish.com/coolboy/archive/2008/09/13/1169992.html
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上面这段评论的意思是说,根据随机因素在整个问题中相对于确定性过程所占比重的大小,我们有两类不同的问题及处理方法。第一类是象对飞行器状态变量的预测及控制类的问题,随机因素是小扰动。第二类是象股市的短期涨落及交通流变化等问题,随机因素占主导地位。马尔可夫过程也可认为是第二类问题的一个例子。湍流问题则刚好介于这两者之中。流体运动本身由确定的纳维-斯托克斯方程很精确地描述,但由于方程的无限维(连续介质)及非线性过程等等而导致的随机因素在湍流问题中也并非是小扰动。由此,对湍流问题的研究即使也是从概率论和随机过程的角度来进行,也与另两类问题的研究方法出现很大的差别。
[ 本帖最后由 coolboy 于 2013-3-9 21:40 编辑 ] |
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