vitsimdemo

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.

成像算法 RD、RMA、CS 的 matlab程序(Imaging algorithm RD, RMA, CS The matlab program)

Kalman Filtering- Theory and Practice Using MATLAB 2008年第三版的随书源码(Kalman Filtering-Theory and Practice Using MATLAB the third edition CD)

image processing it will process the image

资料中介绍了几种经典信噪比估计算法的编码并进行比较，包括误差矢量幅值法、最大似然估计、迭代法以及奇异值分解法。(Data introduces several classical coding SNR estimation algorithm and compared, including error vector magnitude, maximum likelihood estimation, iterative method and singular value decomposition.)

只要检测到反电势过零点就可以得到转子的位置信号，就可以实现无刷直流电机的无位置传感器控制。(As long as the position of the rotor is detected by detecting the zero-crossing of the back-EM potential, the position sensorless control of the brushless DC motor can be realized.)

通信系统MATLAB仿真.ppt (MATLAB simulation of communication systems. Ppt)

一个例子滤波应用程序，可以了解粒子滤波的工作原理(an example of filtering applications, particle filter can understand the principle)

Dimension reduction using principal component analysis.

O Reilly.Practical.C++.Programming.chm,电子书籍，英文版。(O Reilly.Practical.C++.Programming.chm,E-books, in English.)