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2008

Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution

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Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of conservation law equations is proposed. This framework allows us to demonstrate an intrinsic link between microscopic and macroscopic models for evolution via the concept of perturbed generalized dynamic systems. The main result is exemplified with a number of illustrative examples where efficient numerical approximations follow directly from network-based computational models, viewed here as Markov chain approximations. Finally, stability and consistency conditions of such computational models are discussed.
Roderick V. N. Melnik
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2008
Where AMC
Authors Roderick V. N. Melnik
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