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8 .30 Draw the freque ncy-domai n network and calc ul ate V,,(f) in the ci rcui t shown in Fig. P8.30 if isC I) is

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1. The use o linearization as, for example, in Duttweiler (1982), Walach and Widrow (1984), f Gibson and Gray (1988), Sethares (1992), and Douglas and Meng (1994a). In this method of analysis, the error nonlinearity is linearized around an operating point and higher-order terms are discarded. Analyses that are based on this technique fail to accurately describe the adaptive filter performance at the early stages of adaptation where the error usually assumes larger values. 2. Restricting the class of ermr nonlinearities as, e.g., in Claasen and Mecklenbrauker (1981), Mathew and Cho (1987), Bershad (1988), Tanrikulu and Chambers (1996), and Chambers, Tanrikulu, and Constantinides (1994). In this case, the analysis is restricted to particular classes of algorithms such as the sign-error LMS algorithm, the least-mean mixed-norm (LM MN) algorithm, the least-mean fourth (LMF) algorithm, and error saturation nonlinearities. By limiting the study to a specific nonlinearity or to a class of nonlinearities, it is possible to avoid linearization and the results become more accurate. 3. Imposing statistical assumptions on the distribution o the error signals as, e.g., in Koike f (1995) where the elements of the weight-error vector were assumed to be jointly Gaussian

Lemma 5.9 The coeficients o an implicit RK method (b,c , A ) dejined by collocation f satisb the conditions (5.90)

G.F.B. Riemann was a German mathematician who, besides other important achievements, introduced topological methods into the theory of complex functions, studied the representation of functions by trigonometric series, and established new foundations of geometry which were used later in relativity and cosmology. The Riemann hypothesis, related to the prime number theory, remains one of the most famous unsolved problems of modem mathematics. Consider the one-dimensional linear hyperbolic equation (1.1 17),

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