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Dynamic logic (DL) approach establishes a unified framework for the statistical description of mixtures using model-based neural networks. In the present work, we extend the previous results to dynamic processes where the mixture parameters, including partial and total energy of the components are time-dependent. Equations are derived and solved for the estimation of parameters which vary in time. The results provide optimal approximation to a broad class of pattern recognition and process identification problems with variable and noisy data. The introduced methodology is demonstrated on the example of identification of propagating phase gradients generated by intermittent fluctuations in non-equilibrium neural media.
Date of Conference: 12-17 Aug. 2007