Plot the learning curve

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Consider the problem of performing system identification as shown previously in Figure 9.49. Suppose the system to be identified is the following IIR filter.

140_4a7f1272-6d93-4e93-b151-d79dad26b7b2.png

Write a MATLAB program that uses the FDSP toolbox function f lmscorr to identify a model of order m = 50 for this system. Use an input consisting of N = 2000 samples of white noise uniformly distributed over [-1, 1], a relative step size of α = 1, and the default smoothing parameter β.

(a) Plot the learning curve.

(b) Plot the step sizes.

(c) Plot the magnitude response of H(z) and W(z) on the same graph using a legend where W(z) is the adaptive filter using the final values for the weights.

Reference no: EM131325681

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