Topic
Recursive least squares filter
About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.
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TL;DR: In this paper, a systematic method is proposed for the design of general multivariable controller for complex processes to achieve the goal of fast loop responses with acceptable overshoots and minimum loop interaction while maintaining low complexity of the feedback controller.
63 citations
01 Jan 1972
TL;DR: In this paper, the authors proposed a take down policy to remove access to the work immediately and investigate the claim. But they did not provide details of the claim and did not investigate the content of the work.
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63 citations
TL;DR: The simulation studies indicate that the proposed algorithms can effectively estimate the parameters of the C-ARMA models.
Abstract: This paper presents a two-stage least squares based iterative algorithm, a residual based interactive least squares algorithm and a residual based recursive least squares algorithm for identifying controlled autoregressive moving average (C-ARMA) models. The simulation studies indicate that the proposed algorithms can effectively estimate the parameters of the C-ARMA models.
63 citations
TL;DR: In this article, a real-coded genetic algorithm (RGA) was used to identify the parameters of a slider-crank mechanism, and the results of numerical simulations and the experiments proved that the identification method is feasible.
63 citations
TL;DR: A framework for a class of coupled principal component learning rules, in which eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations, and a rule with explicit renormalization of the weight vector length is established.
Abstract: A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.
63 citations