Hi, I have the following setup: x --->[ G(z) ]----->[ (.)^3 ]----->[ H(z) ]-----> y where x is a Gaussian input with E(x)=0 and autocorrelation Rxx. For simplicity, I can set Rxx=sigma^2*delta(tau) and G(z)=1, i.e. a pure Hammerstein system driven with white Gaussian noise (I would like to generalize afterwards if possible): x --->[ (.)^3 ]----->[ H(z) ]-----> y Now I would like to obtain the error in terms of its normalized mean squared error caused by this system: error = E((y-x)^2)/E(x^2) When H(z)=1, this is trivial: E((x^3)^2)/E(x^2) = 15*sigma^4. With H(z) != 1, I think maybe Bussgang's theorem can help but I do not see how. As it is written in Wikipedia, it works for Wiener systems but not Hammerstein. But I found in [1]: "For a Gaussian input signal, the iterative approach can be motivated by Bussgang's theorem. This result implies that for a Hammerstein system, the LTI model that minimizes the mean-square error E((y(t) − G(q)u(t))^2) will be equal to a scaled version of the LTI part of the system." This implies to me that the best fit is obtained by C*H(z) where C is obtained using the formula from Wikipedia [2]: C = 3*sigma^2. However, a quick MATLAB check shows that something is wrong: N = 2^16; sigma = 1; C = 3*sigma^2; b = fir1(10, 0.1); x = sigma*randn(N,1); y1 = filter(b, 1, x.^3); y2 = C*filter(b, 1, x); plot([y1 , y2]) y1 and y2 are way too different, std(y1) and std(y2) completely different. Is there any mistake. Or is there a better approach in the first place to solve this problem? Thanks, Peter [1] Martin Enquist, "Identification of Hammerstein Systems Using Separable Random Multisines" [2] https://en.wikipedia.org/wiki/Bussgang_theorem

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11/18/2016 9:34:00 AM