583 research outputs found
Blind Identification via Lifting
Blind system identification is known to be an ill-posed problem and without
further assumptions, no unique solution is at hand. In this contribution, we
are concerned with the task of identifying an ARX model from only output
measurements. We phrase this as a constrained rank minimization problem and
present a relaxed convex formulation to approximate its solution. To make the
problem well posed we assume that the sought input lies in some known linear
subspace.Comment: Submitted to the IFAC World Congress 2014. arXiv admin note: text
overlap with arXiv:1303.671
Nonlinear Model Predictive Control Of A Distillation Column Using Hammerstein Model And Nonlinear Autoregressive Model With Exogenous Input.
Turus penyulingan adalah unit proses penting dalam industri penapisan petroleum dan kimia. Ia perlu dikawal hampir dengan keadaan-keadaan pengendalian yang optima demi insentif- nsentif ekonomi.
Distillation column is an important processing unit in petroleum refining and chemical industries, and needs to be controlled close to the optimum operating conditions because of economic incentives
Least squares-based iterative identification methods for linear-in-parameters systems using the decomposition technique
By extending the least squares-based iterative (LSI) method, this paper presents a decomposition-based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an interval-varying D-LSI algorithm for handling the identification problems of missing-data systems. The basic idea is to apply the hierarchical identification principle to decompose the original system into two fictitious sub-systems and then to derive new iterative algorithms to estimate the parameters of each sub-system. Compared with the LSI algorithm and the interval-varying LSI algorithm, the decomposition-based iterative algorithms have less computational load. The numerical simulation results demonstrate that the proposed algorithms work quite well
Sparse Nonlinear MIMO Filtering and Identification
In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulation
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