Approximation of an Analog Diffusion Network with Applications to Image Estimation |
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Authors: | G Yin P A Kelly M H Dowll |
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Institution: | (1) Department of Mathematics, Wayne State University, Detroit, Michigan;(2) Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts;(3) PrairieComm, Rolling Meadows, Illinois |
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Abstract: | This work is concerned with a numerical procedure for approximating an analog diffusion network. The key idea is to take advantage of the separable feature of the noise for the diffusion machine and use a parallel processing method to develop recursive algorithms. The asymptotic properties are studied. The main result of this paper is to establish the convergence of a continuous-time interpolation of the discrete-time algorithm to that of the analog diffusion network via weak convergence methods. The parallel processing feature of the network makes it attractive for solving large-scale optimization problems. Applications to image estimation are considered. Not only is this algorithm useful for the image estimation problems, but it is widely applicable to many related optimization problems. |
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Keywords: | diffusion networks image estimation global optimization simulated annealing weak convergence |
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