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
More filters
Proceedings Article
Two-stage second order training in feedforward neural networks
Melvin Robinson,Michael T. Manry +1 more
TL;DR: A new 2nd order two-stage algorithm called OWO-Newton, comparable to Levenberg-Marquardt and having the advantage of reduced computational complexity is developed and shown to have a form of affine invariance.
Journal ArticleDOI
Single-layer networks for nonlinear system identification
TL;DR: The orthogonal least squares selection algorithm has proved to be an effective and reliable off-line optimization tool for single-layer network-type models of any representation, but it suffers from high calculation loads.
Journal ArticleDOI
One day prediction of nighttime VLF amplitudes using nonlinear autoregression and neural network modeling
Hendy Santosa,Yasuhide Hobara +1 more
TL;DR: In this paper, the authors carried out the prediction of daily nighttime mean very low frequency (VLF) amplitude by using Nonlinear Autoregressive with Exogenous Input Neural Network (NARX NN), which was built based on the daily input variables of various physical parameters such as stratospheric temperature, total column ozone, cosmic rays, Dst, and Kp indices.
Proceedings ArticleDOI
Analysis of stochastic gradient identification of polynomial nonlinear systems with memory
TL;DR: This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations with a small sensitivity to the observation noise.
Dissertation
Macromodeling of nonlinear driver and receiver circuits
TL;DR: The contribution of this thesis is to generate black-box macromodels of driver/receiver circuits that result in huge computational speed-up compared to actual transistor-level driver/ receiver circuits and at the same time maintain high accuracy.
References
More filters
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.