GUI_2_EXE_yincangDOS

Simulated Annealing for solving TSP

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Shows a 3d shape and its centerline

利用古典显式差分格式求解抛物型偏微分方程(parabolic partial differential equation)

模态参数的时域识别方法，即在时间域内识别试验结构模态参数。(Time-domain modal parameter identification method, that is, the structure in time domain modal parameter identification experiments.)

说明：  极其实用的matlab小程序 初学者必备(Very useful little program for beginners essential matlab)

adaptive rationalfilter program

现代数字信号处理中的lms及kalman算法的源代码(Lms and kalman modern digital signal processing algorithm source code)

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.

利用自然梯度算法，分数阶傅里叶变换计算方面,可以实现模式识别领域的数据的分类及回归。( Use of natural gradient algorithm, Fractional Fourier transform computing, You can achieve data classification and regression pattern recognition.)