zaibogenzong

This file includes kalman filter and induction motor model

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.

用于abaqus各向同性线弹性梁 单轴拉伸 子程序umat(FOR ISOTROPIC ELASTICITY)

用 matlab 寫的指紋辨識，有需要的下載去研究(Matlab write with fingerprint recognition, there is a need to download to study)

Matlab智能算法的ppt集合 描述了各类智能算法的小算例(Matlab intelligent algorithms ppt collection of various types of intelligent algorithm describes a small study)

an example of ABCD matrix

matlab算法中第8章内容,其他部分陆续上传中，请稍后(matlab algorithm in Chapter 8, other parts of the succession Uploading, please wait)

text and plate recognition system

论文《基于BP神经网络的PID控制器及其MATLAB仿真》Base_on_BP_neural_networ_ PID_controller_and_MATLAB_simulation.rar(Base on BP neural network PID controller and MATLAB simulation)

基于matlab下simulink的模糊控制(fuzzy control)