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By U.A.Bakshi, A.P.Godse

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The truncated SVD as a method of regularization,” BIT 27, 534-553. , and Psaltis, D. (1986). “Storage capacity of holographic associative memories,” Optics Lett. 11, 812-814. Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities,” Proc. Nail. Acad. Sci. USA 79, 2554-2558. Hopfield, J. J. (1984). “Neurons with graded response have collective computational properties like those of two state neurons,” Proc. Natl. Acad. Sci. USA 81, 3088-3092.

ABBISS et al. speeds are possible; with optical hardware and parallel memory addressing, memory bottlenecks to processing speed should also be avoided. VLSI technology with parallel interconnections between one-dimensional arrays of neurons is possible. However, optical or opto-electronic implementations permit two-dimensional systems to be realized; larger or more complex systems can also be implemented in this way. Optical implementation of a neural processor has been widely discussed in the literature, but few systems of any real use have yet emerged.

Cross validation (Nashed and Wahba, 1974) can be also used to provide an estimate for this key parameter. , optimal) p. An interesting point to note is that as ,f3 continues to increase, the matrix to be inverted becomes more and more similar to a unit (or identity) matrix. From Eq. (36) it can be seen that the reconstructed image will then be no better than the original image data g, but now truncated to the support in reconstruction space. If the regularization parameter is allowed to be too small, the ill-conditioning renders the estimate of the reconstructed image useless.

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