Results 231 to 240 of about 113,247 (266)

A Predictive Model of Nonlinear System Based on Generalized Regression Neural Network

2005 International Conference on Neural Networks and Brain, 2006
Generalized regression neural network (GRNN) is usually applied to the function approximation. Based on the principle of GRNN, this paper presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the training and predicting process of the nonlinear model.
null Yibin Song, null Ying Ren
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Nonlinear Regression, Quasi Likelihood, and Overdispersion in Generalized Linear Models

The American Statistician, 1998
Abstract The aim of this article is to reconsider the methods for handling of overdispersion in generalized linear models proposed by McCullagh and Nelder. Our starting point will be a nonlinear regression model with normal errors, specified by a mean function, a variance function and a matrix of covariates.
openaire   +1 more source

Generalized multiple-regression techniques with interaction and nonlinearity for system identification in biological treatment processes

ISA Transactions, 1992
A class of multiple regression models, called "generalized multiple-regression" (GMR) is proposed. GMR has the advantages of being easy and rapid to fit, and uses standard multilinear regression software. It has an advantage over ARIMA models in modeling nonlinearity and linear and nonlinear interactions among variables.
D A, Vaccari, C, Christodoulatos
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Generalized Least Squares, Taylor Series Linearization and Fisher's Scoring in Multivariate Nonlinear Regression

Journal of the American Statistical Association, 2001
In this article, we consider a general multivariate nonlinear regression setting in which the marginal mean and variance–covariance structure share a common set of regression parameters. Estimation is carried out via iteratively reweighted generalized least squares (IRGLS) that entails repeated application of Taylor series linearization and estimated ...
Vonesh E. F, Wang H., Majumdar D.
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A generalized-constraint neural network model: Associating partially known relationships for nonlinear regressions☆

Information Sciences, 2009
In an attempt to enhance the neural network technique so that it can evolve from a ''black box'' tool into a semi-analytical one, we propose a novel modeling approach of imposing ''generalized constraints'' on a standard neural network. We redefine approximation problems by use of a new formalization with the aim of embedding prior knowledge explicitly
Bao-Gang Hu, Han-Bing Qu, Yong Wang, Shuang-Hong Yang   +3 more
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Consistency of M-estimates in general nonlinear regression models

2009
Nonlinear regression model with continuous time and weak dependent or long-range dependent stationary noise is considered. Strong consistency suffient conditions of M-estimates of regression parameters are obtained.
Ivanov, A.V., Orlovsky, I.V.
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Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation

Technometrics, 1970
A principal objective of this paper is to discuss a class of biased linear estimators employing generalized inverses. A second objective is to establish a unifying perspective. The paper exhibits theoretical properties shared by generalized inverse estimators, ridge estimators, and corresponding nonlinear estimation procedures. From this perspective it
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Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming

IEEE Transactions on Evolutionary Computation, 2009
This paper presents a novel approach to generate data-driven regression models that not only give reliable prediction of the observed data but also have smoother response surfaces and extra generalization capabilities with respect to extrapolation. These models are obtained as solutions of a genetic programming (GP) process, where selection is guided ...
Vladislavleva, E.J., Smits, G.F., den Hertog, D.   +2 more
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General Nonlinear Function-on-Function Regression via Functional Universal Approximation

Journal of Computational and Graphical Statistics, 2023
Ruiyan Luo, Xin Qi
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Nonlinear Regression of Ink Spreading Using a General Ellipse Model

2022 22nd International Conference on Control, Automation and Systems (ICCAS), 2022
H. Kim, J. Kim, M. Kwon
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