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(Neural Computation. 1999;11:1475-1491.)
© 1999 The MIT Press

Letter

Learning to Design Synergetic Computers with an Extended Symmetric Diffusion Network

Koji Okuhara

Department of Industrial and Systems Engineering Faculty of Engineering, Hiroshima University, Higashi-Hiroshima-shi, 739-8527 Japan

Shunji Osaki

Department of Industrial and Systems Engineering Faculty of Engineering, Hiroshima University, Higashi-Hiroshima-shi, 739-8527 Japan

Masaaki Kijima

Faculty of Economics, Tokyo Metropolitan University, Hachiohji, Tokyo, 192-0397 Japan

This article proposes an extended symmetric diffusion network that is applied to the design of synergetic computers. The state of a synergetic computer is translated to that of order parameters whose dynamics is described by a stochastic differential equation. The order parameter converges to the Boltzmann distribution, under some condition on the drift term, derived by the Fokker-Planck equation. The network can learn the dynamics of the order parameters from a nonlinear potential. This property is necessary to design the coefficient values of the synergetic computer. We propose a searching function for the image processing executed by the synergetic computer. It is shown that the image processing with the searching function is superior to the usual image-associative function of synergetic computation. The proposed network can be related, as a special case, to the discrete-state Boltzmann machine by some transformation. Finally, the extended symmetric diffusion network is applied to the estimation problem of an entire density function, as well as the proposed searching function for the image processing.






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Copyright © 1999 by The MIT Press.