hankel_var1

pca分类程序，主要用于脑电信号的分类。具有较好的分类精度！(pca classification procedures, mainly for the classification of EEG signals. Has better classification accuracy!)

以中等纯度的精馏塔为研究对象，考虑到不等分子溢流的影响和非理想的汽液平衡，可以得到塔顶产品轻组分含量Y与回流量L之间的传递函数为： 控制要求： 1.采用积分分离 PID控制算法将塔顶轻组分含量Y控制在0.99 2.采用继电法整定PID参数 3.整定效果验证：当被控过程参数时变时，如滞后时间由4→8，开环增益由3.4→6时, 讨论PID控制的响应速度及鲁棒性问题，考察当系统参数发生改变时，上述PID参数是否选取合适。 (Moderate purity fractionator as the object of study, taking into account the ranging molecular overflowing and non-ideal vapor-liquid equilibrium can be obtained by the transfer function between the content of light components of the overhead product Y and back to the flow L: Control Requirements: 1. integral separation PID control algorithm to control the top of the tower light component content Y 0.99 2. relay Law tuning PID parameters. tuning effect is verified: change When accused of process parameters, such as the lag time from 4 → 8, the open loop gain discussed from 3.4 → 6, the PID control of the response speed and robustness issues investigated when the system parameters are changed, the PID parameters select appropriate.)

function varargout = fuzzytool1(varargin)

latex beginers ebook

说明：  大林算法的阶跃响应程序，基于大林算法，解决了振铃问题，(Dahlin step response procedures, based on Dahlin algorithm to solve the ringing problem)

Frequency-resolved optical gating

試做錄音.讀音訊檔 並做FFT轉換和端點偵測(介面) MATLAB程序(Try to do the recording. Reading audio files and do the the FFT conversion and endpoint detection (interface) MATLAB program)

用matlab 7.0编写的滑模变结构仿真的例子。(S-function for continuous state equation function [sys,x0,str,ts]=s_function(t,x,u,flag) switch flag, Initialization case 0, [sys,x0,str,ts]=mdlInitializeSizes Outputs case 3, sys=mdlOutputs(t,x,u) Unhandled flags case {2, 4, 9 } sys = [] Unexpected flags otherwise error([ Unhandled flag = ,num2str(flag)]) end mdlInitializeSizes function [sys,x0,str,ts]=mdlInitializeSizes sizes = simsizes sizes.NumContStates = 0 sizes.NumDiscStates = 0 sizes.NumOutputs = 3 sizes.NumInputs = 1 sizes.DirFeedthrough = 1 sizes.NumSampleTimes = 0 sys=simsizes(sizes) x0=[] str=[] ts=[] function sys=mdlOutputs(t,x,u) x11d=sin(pi*t/2) x21d=cos(pi*t) x31d=1 sys(1)=x11d sys(2)=x21d sys(3)=x31d )

The main idea behind this book, and the incentive for writing it, is that strong connections exist between adaptive filtering and signal analysis, to the extent that it is not realistic—at least from an engineering point of view—to separate them. In order to understand adaptive filters well enough to design them properly and apply them successfully, a certain amount of knowledge of the analysis of the signals involved is indispensable. Conversely, several major analysis techniques become really efficient and useful in products only when they are designed and implemented in an adaptive fashion. This book is dedicated to the intricate relationships between these two areas. Moreover, this approach can lead to new ideas and new techniques in either field.

genetic algorithm to reduce mean squared error in neural network learning process.