Orthogonal least squares methods and their application to non-linear system identification
TLDR
Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram- Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed.Abstract:
Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram-Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed. The classical Gram-Schmidt, modified Gram-Schmidt, and Householder transformation algorithms are then extended to combine structure determination, or which terms to include in the model, and parameter estimation in a very simple and efficient manner for a class of multivariate discrete-time non-linear stochastic systems which are linear in the parameters.read more
Citations
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Missing Data Imputation With OLS-Based Autoencoder for Intelligent Manufacturing
TL;DR: A novel orthogonal-least-square-based autoencoder is proposed to generate new samples for the imputation of missing values, and it outperforms significantly alternative approaches while the missing ratio is greater than 0.05.
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Formulation of soil angle of shearing resistance using a hybrid GP and OLS method
TL;DR: A prediction model was derived for the effective angle of shearing resistance of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS).
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Control-focused, nonlinear and time-varying modelling of dielectric elastomer actuators with frequency response analysis
William R. Jacobs,Emma Wilson,Tareq Assaf,Jonathan Rossiter,Tony J. Dodd,John Porrill,Sean R. Anderson +6 more
TL;DR: In this article, the authors describe an integrated framework for the identification of control focused, data driven and time-varying DEA models that allow advanced analysis of nonlinear system dynamics in the frequency domain.
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Joint Sparse Recovery Using Signal Space Matching Pursuit
TL;DR: In this paper, a new joint sparse recovery algorithm called signal space matching pursuit (SSMP) is proposed to minimize the subspace distance to the residual space by sequentially investigating the support of jointly sparse vectors.
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Inferring the variation of climatic and glaciological contributions to West Greenland iceberg discharge in the twentieth century
Yifan Zhao,Grant R. Bigg,Steve A. Billings,Edward Hanna,Andrew Sole,Hua-Liang Wei,Visakan Kadirkamanathan,David J. Wilton +7 more
TL;DR: In this article, the authors explore the varying relative importance of ice sheet, oceanic and climatic forcing of iceberg discharge from these areas over the twentieth century, by carrying out sensitivity studies of a non-linear auto-regressive mathematical model of the 48oN time series.
References
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Norman R. Draper,Harry Smith +1 more
TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
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Gene H. Golub,C. Reinsch +1 more
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Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
TL;DR: Recursive input-output models for non-linear multivariate discrete-time systems are derived, and sufficient conditions for their existence are defined.