Radial basis function network calibration model for near-infrared spectra in wavelet domain using a genetic algorithm |
| |
Authors: | Wang Yan Xiang Bingren |
| |
Institution: | a Center for Instrumental Analysis, China Pharmaceutical University, Nanjing 210009, China b School of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China |
| |
Abstract: | Near-infrared (NIR) spectrometry is now widely used in various fields and great attention is paid to the application of it to addressing complex problems, which brings about the need for the calibration of systems that fail to exhibit satisfactional linear relationship between input-output data. In this work we present a novel method to build a multivariate calibration model for NIR spectra, i.e. genetic algorithm-radial basis function network in wavelet domain (WT-GA-RBFN), which combines the advantages of wavelet transform and genetic algorithm. The variable selection is accomplished in two stages in wavelet domain: at the first stage, the variables are pre-selected (compressed) by variance and at the second stage the variables are further reduced by a special designed GA. The proposed method is illustrated through presenting its application to three NIR data sets in different fields and the comparison to PLS model. |
| |
Keywords: | Near-infrared spectrometry Radial basis functions networks Discrete wavelet transform Variable selection Genetic algorithms |
本文献已被 ScienceDirect PubMed 等数据库收录! |
|