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Minimization of this cost function according to the standard gradient descent leads to a simple learning algorithm given by w1ip (k + 1) = w1ip (k) (k) J(w1 ) w1ip (k) + (k) fR [ i (k)] R (k p), x w1ip (8.24)

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Then he came across a far more serious threat. Apparently, large state pension funds, especially in California, had also suffered big losses in UCBS. Plus, they had suffered big losses in WorldCom, Enron, and Global Crossing. Unlike the average investor, however, these pension funds had invested in the bonds, which Harris had also been involved in. The lawyer for some of these funds was a leading corporate ambulance chaser, and he wanted blood. If anyone asked him why, say it was because his clients, the pension fund managers, wanted blood. And if anyone asked them why, they d say it was because they had hundreds of thousands of public employees in California who wanted blood. The employees had lost a big chunk of their retirement because of the likes of Enron, Global Crossing, WorldCom, and now UCBS. These poor people merited fair restitution. They were angry, and they wanted their money back. At first, the lawyer thought he d sue the bankrupt companies,

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Convolutional code design consists of selection of h(u, v) to optimize some metric. While minimization of the mean-square error is not the only appropriate design metric, it is an attractive goal. Since the Wiener error decreases monotonically with h(u, j^ v)j2 , error minimization is achieved by maximizing j^ v)j2 across the target h(u, spatial spectrum. Code design is trivial for focal imaging, where Eqn. (8.22) indicates clear advantages for forming as tight a point spread function as possible. Ideally, one selects h(x, y) d(x, y), such that h(u, v) is constant. As discussed in Section 8.1, however, in certain situations design to the goal h(x, y) d(x, y) is not the best choice. Of course, as discussed in Sections 8.4 and 8.5, one is unlikely to invert using the Wiener lter in such situations. Figure 8.4 illustrates the potential advantage of coding for coded aperture systems by plotting the error of Eqn. (8.22) under the assumption that the signal and noise power spectra are constant. The error decreases as the order of the coded aperture increases, although the improvement is sublinear in the throughput of the mask. The student will, of course, wish to compare the estimation noise of the Wiener lter with the earlier SNR analysis of Eqns. (2.47) and (2.48). The nonuniformity of the SNR across the spectral band illustrated in Fig. 8.4 is typical of linear deconvolution strategies. Estimation error tends to be particularly high in near nulls or minima in the MTF. Nonlinear methods, in contrast, may utilize relationships between spectral components to estimate information even from bands where the system transfer function vanishes. Nonlinear strategies are also more effective in enforcing structural prior knowledge, such as the nonnegativity of optical signals.

Equivalence. Actually, deterministic and stochastic least-squares problems can be related more directly than above and without the need for the Gaussian assumption on the data and noise. In Sec. 31.1 we show that there is an intimate relation between regularized least-squares problems, as studied in this chapter, and linear least-mean-squares estimation problems, as studied in Part I1 (LinearEstimation). Although the former class of problems deals with deterministic variables while the latter class of problems deals with random variables, both classes turn out to be equivalent in the sense that solving a problem from one class also solves a problem from the other class and vice-versa. Among other results, such equivalence statements allow us, for example, to relate Kalman filtering (which deals with stochastic estimation cf. 7) to adaptive RLS filtering (which deals with deterministic estimation) - see Sec. 31.2 and Sayed and Kailath (1994b).

The model of the j t h artificial neuron, shown in Figure 8.4(b) can be described mathein matical terms by the following pair equations: of

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