Complex Process Visualization through Continuous Feature Maps Using Radial Basis Functions
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Correlation Visualization of High Dimensional Data Using Topographic Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Fault Diagnosis Using Wavelet Neural Networks
Neural Processing Letters
Model selection approaches for non-linear system identification: a review
International Journal of Systems Science
Nonlinear dimensionality reduction by locally linear inlaying
IEEE Transactions on Neural Networks
Improved nonlinear PCA based on RBF networks and principal curves
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Model-based predictive control for spatially-distributed systems using dimensional reduction models
International Journal of Automation and Computing
A new principal curve algorithm for nonlinear principal component analysis
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
A generative model and a generalized trust region Newton method for noise reduction
Computational Optimization and Applications
Hi-index | 0.00 |
This paper describes a novel means for creating a nonlinear extension of principal component analysis (PCA) using radial basis function (RBF) networks. This algorithm comprises two distinct stages: projection and self-consistency. The projection stage contains a single network, trained to project data from a high- to a low-dimensional space. Training requires solution of a generalized eigenvector equation. The second stage, trained using a novel hybrid nonlinear optimization algorithm, then performs the inverse transformation. Issues relating to the practical implementation of the procedure are discussed, and the algorithm is demonstrated on a nonlinear test problem. An example of the application of the algorithm to data from a benchmark simulation of an industrial overheads condenser and reflux drum rig is also included. This shows the usefulness of the procedure in detecting and isolating both sensor and process faults. Pointers for future research in this area are also given