Advances in Electronics and Electron Physics, Vol. 87 by Peter W. Hawkes (Ed.)

By Peter W. Hawkes (Ed.)

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14, 462-464. Csiszar, I. (1989). “Why least squares and maximum entropy? An axiomatic approach to inverse problems,” Math. Inst. Hungarian Acad. , No. 19. Darling, A. M. (1984). D. thesis, University of London. Darling, A. , Hall, T. , and Fiddy, M . A. (1983). “Stable, noniterative, object reconstruction from incomplete data using a priori data,” J . O . S . 73, 1466-1469. Donoho, D. , and Johnstone, I. M. (1989). “Projection-based approximation and a duality with kernel methods,” Annuls of’ Statistics 17(1), 58- 106.

120-123. Fiddy, M. A. (1987). “The role of analyticity in image recovery,” in Image Recovery: Theory and Application (H. ), pp. 499-529. Academic Press, Boca Raton, Florida. Fienup, J. R. (1982). “Phase retrieval algorithms: A comparison,” Appl. Opt. 21, 2758-2769. , and Weiss, S. (1989). “Photorefractive oscillators,” IEEE Trans. O E 25, 550-569. Friedman, J. , and Stuetzle, W. (1981). “Projection pursuit regression,” J . Amer. Stat. Assoc. 76, 817-823. 46 J. B. ABBISS et al. , and Geman D. (1984).

Clearly, with the appropriate hardware very high 44 J . B. 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.

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