The multilayer feedforward networks based on the back propagation with maximum likelihood principal component analysis (MLPCA-BP-MLFN) preprocessor were developed to analyze overlapping Osteryoung square wave voltammograms. The principal component analysis back propagation multilayer feed forward networks (PCA-BP-MLFN) and the Kernel Partial Least Squares (KPLS) method were also applied in this paper for comparison. Three programs called PKPLS, PPCABPMLFN and PMLPCABPMLFN were designed to perform the calculations. A comparative study of the prediction capabilities of the three approaches showed the three methods provided satisfactory results. MLPCA-BP-MLFN is a valuable tool in solving the local minimum problem and improving the convergence rate. Comparing with KPLS and PCA-BP-MLFN, MLPCA-BP-MLFN was showed to be improved significantly in the case.
Resolve Overlapping Voltammetric Peaks by Artificial Neural Networks with Maximum Likelihood Principal Component Analysis
2008 Congress on Image and Signal Processing ; 5 ; 507-511
01.05.2008
409422 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Image compression using principal component neural networks
British Library Online Contents | 2001
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