S-Function

matlab 可视化 微分方程 物理 算法 超越方程 科学计算 数学变换 最值问题 图像增强 图形界面 线性方程 元胞自动机 晶体生长 金融(physical algorithm matlab visualization in scientific computing differential equations of mathematical transformations beyond the most value problem of linear equations graphical image enhancement of cellular automata Crystal Growth Finance)

计算王变换的matlab源程序， 对初学者很有用图像变换的程序(calculator the wang transform s programme,vrey useful for you)

人工神经网络算法，matlab,c,fortran(artificial neural network algorithm, Matlab, c, fortran)

F16的六自由的非线性气动模型，用于飞行控制和仿真，matlab编写(F16 six free nonlinear aerodynamic model for flight control and simulation, matlab write)

Matlab连续系统灰色PID跟踪，预测加控制(Matlab for Gray PID tracking system, add the control prediction)

说明：  此程序用来进行信道延迟，和序列互相关的。(This procedure used for channel delay, and the sequence of inter-related.)

机器人运动学和动力学模型，六自由度的，界面设计以及轨迹规划等功能(Robot kinematics and dynamics models, six degrees of freedom, interface design, and trajectory planning, etc.)

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.

Language Implementation Patterns - Terence Parr. How to design your own programming language.

Matlab编程学习课件,非常好的资料赶紧下载(Matlab programming learning courseware, very good information quickly download)