Machine Learning
Fuzzy sets, fuzzy logic, and fuzzy systems
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay
Neural Computation
Recursive principal components analysis using eigenvector matrix perturbation
EURASIP Journal on Applied Signal Processing
On-line fuzzy modeling via clustering and support vector machines
Information Sciences: an International Journal
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
Automatic clinical image segmentation using pathological modeling, PCA and SVM
Engineering Applications of Artificial Intelligence
Evaluation of face recognition techniques using PCA, wavelets and SVM
Expert Systems with Applications: An International Journal
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
Fuzzy modelling via on-line support vector machines
International Journal of Systems Science
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Principal component analysis (PCA) has been widely applied in process monitoring and modeling. The time-varying property of industrial processes requires the adaptive ability of the PCA. This paper introduces a novel PCA algorithm, named on-line PCA (OLPCA). It updates the PCA model according to the process status. The approximate linear dependence (ALD) condition is used to check each new sample. A recursive algorithm is proposed to reconstruct the PCA model with selected samples. Three types of experiments, a synthetic data, a benchmark problem, and a ball mill load experimental data, are used to illustrate our modeling method. The results show that the proposed OLPCA is computationally faster, and the modeling accuracy is higher than conventional moving window PCA (MWPCA) and recursive PCA (RPCA) for time-varying process modeling.