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We develop an abstract approximation and convergence framework for the estimation of random parameters in infinite dimensional dynamical systems governed by regularly dissipative operators in a Gelfand triple setting. Our results are motivated by a problem involving the development of a data analysis system for a transdermal alcohol biosensor. Our approach combines some recent results for random abstract...
We develop a stochastic finite element based scheme for constructing a likelihood function for the Bayesian estimation of parameters in a distributed parameter model for thin film layered organic photovoltaic cells. The scheme is based on a distributed parameter model for the propagation of the optical wave through the various layers of the cell and its conversion to an electrical current in the active...
Persistence of excitation is a sufficient condition for parameter convergence in a class of adaptive, or on-line, identification schemes for dynamical systems. In the case of abstract linear parabolic and hyperbolic distributed parameter systems, this condition requires that a family of bounded linear functionals be norm bounded away from zero. In general, this condition on the plant is difficult...
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