PubMed · 14747901
Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination.
Abstract
This paper presents a novel method, named wavelet packet transform based multilayer feedforward neural network with Levenberg-Marquardt and back propagation algorithm (WPTLMBP), developed for simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet packet domain the quality of noise removal can be improved. The artificial neural network was applied for non-linear multivariate calibration. In this study, by optimization, wavelet packet function, decomposition level and number of hidden nodes for WPTLMBP method were selected as Db2, 2, and 4 respectively. A program PWPTLMBP was designed to perform simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). The relative standard error of prediction (RSEP) for all components with WPTLMBP, LM-BP-MLFN, and PLS methods were 6.39, 10.4, and 8.30%, respectively. Experimental results showed the proposed method to be successful and better than the others.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Shouxin Ren, Ling Gao. 2004-01-28. Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination.. https://doi.org/10.1007/s00216-003-2395-y
Cite the original work for its findings. Save a collection to share your selection of sources.