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Information Entropy Dynamics and Maximum Entropy Production Principle
The asymptotic convergence of probability density function (pdf) and convergence of differential entropy are examined for the non-stationary processes that follow the maximum entropy principle (MaxEnt) and maximum entropy production principle (MEPP). Asymptotic convergence of pdf provides new justification of MEPP while convergence of differential entropy is important in asymptotic analysis of ...
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The aim of the present paper is to investigate the behavior of a single-input single-unit system, learning through the maximum-entropy principle, in order to understand some formal property of Bell-Sejnowski’s PDF-matching neuron. The general learning equations are presented and two casestudy are discussed with details.
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The notion of entropy is widely used in modern statistical physics, thermodynamics, information theory, engineering etc. In 1948, Claude Shannon introduced his information entropy for an absolutely continuous random variable x having probability density function (pdf) p. In 1988, Constantino Tsallis introduced a generalized Shannon entropy. Tsallis entropy have found applications in various sci...
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We consider the finite extensible nonlinear elasticity (FENE) dumbbell model in viscoelastic polymeric fluids. We employ the maximum entropy principle for FENE model to obtain the solution which maximizes the entropy of FENE model in stationary situations. Then we approximate the maximum entropy solution using the second order terms in microscopic configuration field to get an probability densi...
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