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Bayesian estimation for nonlinear systems is still a challenging problem, as in general the type of the true probability density changes and the complexity increases over time. Hence, approximations of the occurring equations and/or of the underlying probability density functions are inevitable. In this paper, we propose an approximation of the conditional densities by wavelet expansions. This kind...
This paper deals with recursive state estimation for nonlinear systems. A new set of sigma-points for the unscented Kalman filter is proposed as well as a way to substitute a nonlinear output with a linear one. The importance of the function of the state which must be estimated is also illustrated and the need for taking it into account when designing the state estimator. All the proposed methods...
This paper investigates the problem of state estimation for nonlinear discrete-time dynamic systems. The estimator is parameterized as a linear combination of chosen basis functions. We seek the parameter that minimizes the mean squared estimation error (MSE); however, computing this objective is intractable. Hence, the MSE is approximated using the scaled unscented transform (SUT), which yields a...
An adaptive neuro-fuzzy controller is proposed in this paper to deal with the problem of tracking nonlinear affine in the control dynamical systems with unknown nonlinearities. The plant is described by means of a Takagi-Sugeno fuzzy model, including dynamic fuzzy rules of generalized form, where the local submodels are realized through nonlinear input-output mappings. Instead of modelling the plant...
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