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This paper presents experiments with tracking time{varying parameters of a dynamic system applied to parameter estimation and smoothing of piecewise continuous function. The smoothing algorithm is based on bayesian computation of the probability distribution over the set of alternative hypotheses describing a set of alternative models of expected (possible)(More)
Kalman filter is a frequently used tool for linear state estimation due to its simplicity and optimality. It can further be used for fusion of information obtained from multiple sensors. Kalman filtering is also often applied to nonlinear systems. As the direct application of bayesian functional recursion is computationally not feasible, approaches commonly(More)
—Kalman filter is a frequently used tool for linear state estimation due to its simplicity and optimality. It can further be used for fusion of information obtained from multiple sensors. Kalman filtering is also often applied to nonlinear systems. As the direct application of bayesian functional recursion is computationally not feasible, approaches(More)
Stability is the first demand of the feedback control design. The parametrization of all stabilizing controllers is standard theory. The stabilizing controller has some free parameters which can be used for further optimization. Linear Quadratic (LQ) control and Dead Beat control are standard algorithms for optimal control of discrete time models of real(More)
Real time system parameter estimation from the set of input-output data is usually solved by the quadratic norm minimization of system equations errors-known as least squares (LS). But measurement errors are also in the data matrix and so it is necessary to use a modification known as total least squares (TLS) or mixed LS and TLS. Instead of quadratic norm(More)