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Example 8.2 The performance of the proposed neural network model will be illustrated by two examples. The three sub-Gaussian source signals shown in Fig. 8.2 have been mixed using the mixing matrix whose rows are h1 = [0.8 0.4 0.9], h2 = [0.7 0.3 0.5], and h3 = [ 0.6 0.8 0.7]. Uncorrelated Gaussian noise signals with variance 1.6 were added to each of the elements of x(k). The neural network model depicted in Fig. 8.4 with associated learning rules in (8.92) (8.93) and nonlinearities f (yi ) = yi , g(yi ) = tanh(10yi ) 2 2 and (ei ) = i ei with i = 1/ i , where i is the variance of noise was used to separate these signals, where H(0) = I. Shown in Fig.8.2 are the resulting separated signals, in which the source signals are accurately estimated. 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