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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Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification
TL;DR: A new family of adaptive wavelet neural networks, called lattice dynamical wavelet Neural networks (LDWNNs), is introduced for spatio-temporal system identification by combining an efficient wavelet representation with a coupled map lattice model.
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When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition With Limited Data
TL;DR: A new deep dictionary learning and coding network (DDLCN) for image-recognition tasks with limited data is presented and empirical results show that DDLCN achieves competitive results compared with state-of-the-art methods when the training data are limited.
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Toward a Principled Methodology for Neural Network Design and Performance Evaluation in QSAR. Application to the Prediction of LogP
TL;DR: A principled methodology for designing neural networks for QSAR and estimating their performances is described and this approach is applied to the prediction of logP, and the results are compared to those obtained on the same molecules by other methods.
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Monthly streamflow prediction in the Volta Basin of West Africa: A SISO NARMAX polynomial modelling
TL;DR: In this article, single-input-single-output (SISO) nonlinear system identification techniques were employed to model monthly catchment runoff at selected gauging sites in the Volta Basin of West Africa.
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Effective connectivity anomalies in human amblyopia.
TL;DR: The effective connectivity in the lateral geniculate nucleus and visual cortex of humans with amblyopia was reduced when driven by the amblyopic eye, suggesting contrary to the current single-cell model of localized signal reduction, that a significant part of the Amblyopic deficit is due to anomalous interactions between cells in disparate brain regions.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.