Abstract
Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention
as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the
optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the
number of nodes, the location of centres, and the weights between the hidden layer and the output layer
can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for
the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA
is that the derived nonlinear scores may not be statistically independent or follow a simple parametric
distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined
probability distribution functions is lost. This paper proposes the use of a support vector data
description and shows that transforming the nonlinear principal components into a feature space allows
a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the
proposed method for solving nonlinear principal component problems, compared with linear PCA and
kernel PCA.
Original language | English |
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Pages (from-to) | 1306-1317 |
Number of pages | 12 |
Journal | Journal of Process Control |
Volume | 21 |
Issue number | 9 |
DOIs | |
Publication status | Published - Oct 2011 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Modelling and Simulation
- Computer Science Applications