This is the first paper that introduces a nonlinearity test for principal component models. The methodology involves the division of the data space into disjunct regions that are analysed using principal component analysis using the cross-validation principle. Several toy examples have been successfully analysed and the nonlinearity test has subsequently been applied to data from an internal combustion engine
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
The main purpose of this paper is a study of the efficiency of different nonlinearity detection meth...
measuring the correlation between test data and finite element results for nonlinear, transient dyna...
Summary form only given as follows. In this paper the term system identification addresses the proce...
In this chapter we present a technique for the analysis of customer satisfaction based on a dimensio...
This book expounds the principle and related applications of nonlinear principal component analysis ...
A new test of normality based on Nonlinear Principal Components (NLPC) is introduced. Our testing pr...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neu...
This paper presents a statistical-based fault diagnosis scheme for application to internal combustio...
The thesis concentrates on property of linearity in time series models, its definitions and possibil...
A structure detection test which distinguishes between linear and nonlinear dynamic effects in the s...
Principal Component Analysis(PCA) reduces the dimensionality of the process by creating a new set of...
Also published in: Journal of Process Control 10(2000), p. 113-123SIGLEAvailable from TIB Hannover: ...
In the field of structural health monitoring or machine condition monitoring, the activation of nonl...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
The main purpose of this paper is a study of the efficiency of different nonlinearity detection meth...
measuring the correlation between test data and finite element results for nonlinear, transient dyna...
Summary form only given as follows. In this paper the term system identification addresses the proce...
In this chapter we present a technique for the analysis of customer satisfaction based on a dimensio...
This book expounds the principle and related applications of nonlinear principal component analysis ...
A new test of normality based on Nonlinear Principal Components (NLPC) is introduced. Our testing pr...
Abstract The idea of summarizing the information contained in a large number of variables by a small...
Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neu...
This paper presents a statistical-based fault diagnosis scheme for application to internal combustio...
The thesis concentrates on property of linearity in time series models, its definitions and possibil...
A structure detection test which distinguishes between linear and nonlinear dynamic effects in the s...
Principal Component Analysis(PCA) reduces the dimensionality of the process by creating a new set of...
Also published in: Journal of Process Control 10(2000), p. 113-123SIGLEAvailable from TIB Hannover: ...
In the field of structural health monitoring or machine condition monitoring, the activation of nonl...
Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for ma...
The main purpose of this paper is a study of the efficiency of different nonlinearity detection meth...
measuring the correlation between test data and finite element results for nonlinear, transient dyna...