MATLAB中ART中的实测投影值如何得到

例如，已知仔闭老tan(x)=√3/3,求x。 程序如下念升： x=atan(sqrt(3)/3)； 运行结果： x = 0.5236 %得到的是弧度； 一般我们习惯用角度来表示。如态蚂下转化： x=x*180/pi运行结果： x = 30.0000 %这就得到了角度。

%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)

%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

%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 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')

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