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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Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets
TL;DR: A new time-varying autoregressive (TVAR) modelling approach is proposed for non-stationary signal processing and analysis, with application to EEG data modelling and power spectral estimation using a novel multiwavelet decomposition scheme.
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Online NARMAX model for electron fluxes at GEO
TL;DR: In this paper, multi-input single-output (MISO) nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been derived to forecast the > 0.8 and > 2 MeV electron fluxes at geostationary Earth orbit (GEO).
Journal ArticleDOI
Dewpoint Pressure Model for Gas Condensate Reservoirs Based on Genetic Programming
TL;DR: In this paper, an orthogonal least squares algorithm (GP-OLS) was used to generate a linear-in-parameters dewpoint pressure model represented by tree structures.
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Two-Stage Mixed Discrete–Continuous Identification of Radial Basis Function (RBF) Neural Models for Nonlinear Systems
Kang Li,Jian-Xun Peng,Er-Wei Bai +2 more
TL;DR: A unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous identification procedure for nonlinear dynamic systems using radial basis function (RBF) neural models.
Journal ArticleDOI
A physics-driven neural networks-based simulation system (phynness) for multimodal interactive virtual environments involving nonlinear deformable objects
TL;DR: The PhyNNeSS method distinguishes itself from previous efforts in that a systematic physics-based precomputational step allows training of neural networks which may be used in real-time simulations, and is scalable, with the accuracy being controlled by the number of neurons used in the simulation.
References
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Book
Applied Regression Analysis
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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Singular value decomposition and least squares solutions
Gene H. Golub,C. Reinsch +1 more
TL;DR: The decomposition of A is called the singular value decomposition (SVD) and the diagonal elements of ∑ are the non-negative square roots of the eigenvalues of A T A; they are called singular values.
Book
Linear regression analysis
TL;DR: In this paper, the authors take into serious consideration the further development of regression computer programs that are efficient, accurate, and considered an important part of statistical research, and provide up-to-date accounts of computational methods and algorithms currently in use without getting entrenched in minor computing details.
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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.