In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be describ...
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In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametricestimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametricestimation of a stochastic interconnected nonlinear hydraulic system is treated.
This document aims to solve the parametricestimation problem of nonlinear time-varying systems modeled by Wiener-Hammerstein models (W-H) with unknown time-varying parameters. A recursive instrumental variable estima...
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ISBN:
(纸本)9781509034079
This document aims to solve the parametricestimation problem of nonlinear time-varying systems modeled by Wiener-Hammerstein models (W-H) with unknown time-varying parameters. A recursive instrumental variable estimation method is developed in order to estimate the parameters of the considered blocks-oriented models despite the existence of a correlated noise with observations. The estimation method is based on the least square technique. Furthermore, an example is devoted to appear and justify the availability of the detailed and proposed method.
In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a sum of 2-dimesional sinuso...
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ISBN:
(纸本)0819431958
In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a sum of 2-dimesional sinusoidal signals with quadratic phase errors, which are caused by "range walk" and "variable range rate" respectively. Based on the data model, we propose a parametric RELAX-based algorithm to extract the target features when there are MTRC errors in radar imaging. The algorithm minimizes a complicated nonlinear least-squares(NLS) cost function,and it is performed alternately by letting only the parameters and errors of one scatterer vary and freezing all others at their most recently determined values. The Cramer-Rao bounds(CRB's) for the parameters of the data model are also derived. We compare the performance of the proposed algorithm with the CRB's by simulation. And the results show that the mean squared errors of the parameter estimates obtained by the algorithm can approach the corresponding CRB's. Then we apply the algorithm to the simulated radar data with MTRC errors. The proposed algorithm generates "focused" point image with higher resolution, which conforms the algorithm and the data model.
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