AGC_NeTe1k2

IIR一阶低通滤波器 一阶高通滤波器 Notch 嵌波滤波器 Peak 滤波器 IIR低通滤波（Butterworth） IIR高通滤波（Butterworth） IIR带通滤波（Butterworth） IIR带阻滤波（Butterworth） IIR低通滤波（chebyshev 1） IIR低通滤波（chebyshev 1） chebyshev 中用到的函数 cheby.m(First-order IIR low-pass filter the first-order high-pass filter Notch filter inlay Peak wave filter IIR low-pass filter (Butterworth) IIR high-pass filtering (Butterworth) IIR band-pass filter (Butterworth) IIR band-stop filter (Butterworth) IIR low-pass filter (chebyshev 1) IIR low-pass filter (chebyshev 1) chebyshev function used cheby.m)

一个自适应广义模型预测控制的编程例子，里面有详细的步骤(Adaptive generalized model predictive control of a programming example, which has detailed steps)

使用最小二乘法对任意位置的椭圆进行数据拟合的程序，使用全部的边缘点集。(Anywhere using the least squares method for data fitting ellipse, the use of all of the edge point set.)

THIS IS THE SOURCE CODE FOR PID CONTROLLER

三相电力网络的无功补偿的设计，基于matlab的三相负荷的无功补偿(static var compensator)

this is a basic sff code about SML METHOD.

Fuzzy flip flop, basic code - an example

FCM fuzzy C-MEAns in MATLABn

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.

这是一种用matlab来实现kalman滤波的演示例子。(this is an exmaple for kalman language s use.)