Model-Predictive-Control

位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。 网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）= 取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为 y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2) 始值为，kp=20， kd=0.3， ki=0.1。 (Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)

This paper discusses the impact of HVDC on Power System Stability and proposes a new control mechanism based on Fuzzy set theory to augment dynamic performance of a multi-machine power system. To have good damping characteristics over a wide range of operating conditions, deviations in speed and acceleration of the machines are chosen as the input signals to the fuzzy controller. These input signals are first characterized by a set of linguistic variables using fuzzy set notations .The fuzzy relation matrix allows a set of fuzzy logic operations that are performed on controller inputs to obtain the desired output. The effectiveness of the proposed controller is demonstrated by a multi-machine system example. The performance of this fuzzy controller, in comparison to the conventional fixed gain controller, demonstrates the efficacy of this new fuzzy PID controller. Index Terms—HVDC, Eliminated variable

短时傅里叶中通过各种不同的窗来看窗的归一化频率(Short-time Fourier window in the view through a variety of different normalized frequency window)

梅尔倒频谱，语音识别的相关代码，以及相关注释(Mel cepstrum, speech recognition-related code, and related notes)

雨流法的matlab程序 可用于疲劳计算，适用于机械和相关行业(Rainflow matlab program can be used for fatigue calculation for machinery and related industries)

是基于四阶累积量的旋转不变因子ESPRIT算法，在matlab环境下实现(Is based on the cumulative amount of rotation of fourth-order constant factor ESPRIT algorithm in matlab environment to achieve)

这是有关MATLAB学习的一些编程技巧和使用的一个程序示例(MATLAB STUDY)

说明：  蒙特卡罗模拟随机数是最好的方式，本程序设计在552nm脉冲后，再一左一右的657nm冷却脉冲，测试激发达到100 的概率，尤其考查蓝、绿及近红外波段(MATLAB 6.5 Monte Carlo simulation cool3.m This simulation cycles "num" number of atoms "steps" times, with a cooling sequence of a 657 nm cooling pulse from the right, then a 657 nm cooling pulse from the left, followed by a 552 nm quenching pulse. Then the sequence is repeated with cooling pluses first incident from the left. Switching the pulse direction order helps maintain symmetery in the resultant velocity distribution. The probability of quenching the excited state is no longer 100 , it is "percent" Random blue, IR, and green photon recoil kicks have been included in the simulation due to the quenching. )

Gaussian_Window,Fourier_Transform的matlab代码例程(matlab code about Gaussian_Window,Fourier_Transform)

CankNicolson scheme to solve the diffusion equation for different initial conditions