入门基础操作
一、命令窗基本操作
1、format loose:稀疏显示
2、format compact:紧凑显示
3、format rat:分数显示
4、clear:清除工作空间
5、quit、exit:退出软件
6、help:显示已有的函数和对应的目录
二、让你的代码更具有可读性
M文件
运用cell module
举例:代码如下
%% Program Start %lms算法源程序 clear all close all %channel system order sysorder = 5 ; % Number of system points N=2000; inp = randn(N,1); n = randn(N,1); [b,a] = butter(2,0.25); Gz = tf(b,a,-1);%传递函数 %This function is submitted to make inverse Z-transform (Matlab central file exchange) %The first sysorder weight value %h=ldiv(b,a,sysorder)'; % if you use ldiv this will give h :filter weights to be h= [0.0976; 0.2873; 0.3360; 0.2210; 0.0964;]; y = lsim(Gz,inp); %% add some noise n = n * std(y)/(10*std(n)); d = y + n; totallength=size(d,1); %Take 60 points for training N=60 ; %% % <http://www.mathworks.com MathWorks> %% begin of algorithm w = zeros ( sysorder , 1 ) ; for n = sysorder : N u = inp(n:-1:n-sysorder+1) ; y(n)= w' * u; e(n) = d(n) - y(n) ; % Start with big mu for speeding the convergence then slow down to reach the correct weights if n < 20 mu=0.32; else mu=0.15; end w = w + mu * u * e(n) ; end %% check of results for n = N+1 : totallength u = inp(n:-1:n-sysorder+1) ; y(n) = w' * u ; e(n) = d(n) - y(n) ; end hold on plot(d) plot(y,'r'); title('System output') ; xlabel('Samples') ylabel('True and estimated output') figure semilogy((abs(e))) ; title('Error curve') ; xlabel('Samples') ylabel('Error value') figure plot(h, 'k+') hold on plot(w, 'r*') legend('Actual weights','Estimated weights') title('Comparison of the actual weights and the estimated weights') ; axis([0 6 0.05 0.35]) %% RLS Algorithm 算法 randn('seed', 0) ; rand('seed', 0) ; NoOfData = 8000 ; % Set no of data points used for training Order = 32 ; % Set the adaptive filter order Lambda = 0.98 ; % Set the forgetting factor Delta = 0.001 ; % R initialized to Delta*I x = randn(NoOfData, 1) ;% Input assumed to be white h = rand(Order, 1) ; % System picked randomly d = filter(h, 1, x) ; % Generate output (desired signal) % Initialize RLS P = Delta * eye ( Order, Order ) ; w = zeros ( Order, 1 ) ; % RLS Adaptation for n = Order : NoOfData ; u = x(n:-1:n-Order+1) ; pi_ = u' * P ; k = Lambda + pi_ * u ; K = pi_'/k; e(n) = d(n) - w' * u ; w = w + K * e(n) ; PPrime = K * pi_ ; P = ( P - PPrime ) / Lambda ; w_err(n) = norm(h - w) ; end ; %% Plot results figure ; plot(20*log10(abs(e))) ; title('Learning Curve') ; xlabel('Iteration Number') ; ylabel('Output Estimation Error in dB') ; figure ; semilogy(w_err) ; title('Weight Estimation Error') ; xlabel('Iteration Number') ; ylabel('Weight Error in dB') ;
操作如下:
1、在每段开头使用 (%%+空格+每段代码的名字)
2、发布
让运行后的代码更具备可读性(会把运行结果加到相应的代码后面)
效果如下:file:///D:/MATLAB/Matlab002_WHaoL/html/WHaoL_MLS_RLS.html
:D:/MATLAB/Matlab002_WHaoL/html/WHaoL_MLS_RLS.html