PureWienerIdentification

# PureWienerIdentification - % % % % % We will use only a...

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%% We will use only a filter considering it a wiener model. (that is : use %% outputs instead of inputs fore noise to examine %% colored noise effects And it seems that for extreme values of sigma the %% algorithm performs satisfactorily. % clear all u=normrnd(0,2,1,800); % A white gaussian input sequence u with length % 400 0 mean and standard deviation 2 e =normrnd(0,.2,1,1289); % this is added after all. actually it should have ic = i; % been done before rts = [.98*exp(ic) .98*exp(-ic) -.75 ]; % to be added .98*exp(1.6*ic) .98*exp(- 1.6*ic) .95*exp(2.5*ic) .95*exp(-2.5*ic) a = poly(rts); % ai s b = [1 .8 .3 .4] ; % bi s % now we will get the input output data. [h,tt] = impz(b,[a]); %filter impulse response us = [0 u(1:end-1)]; % past values of "u" v = conv(h,u); y =v+e;%; % y=(sinc(v).*v.^2); figure(1);subplot(3,1,1) ; plot(u(1:700)); title('input to the system'); subplot(3,1,2) ; plot(v(1:700)); title('output of the filter: before nonlinearity: v');

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## PureWienerIdentification - % % % % % We will use only a...

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