Quantitative multivariate analysis with artificial neural networks

Chii Wann Lin*, Tzu-Chien Hsiao, Mang Ting Zeng, Hue Hua Chiang

*此作品的通信作者

研究成果: Paper同行評審

1 引文 斯高帕斯(Scopus)

摘要

Quantitative interpretation of spectra can be achieved by using artificial neural networks with multi-layer architecture. Both back-propagation (BP) and radial basis function (RBF) are implemented and tested with raw absorption spectra and normalized spectra of glucose solutions in MATLAB. Simulation results showed partial least square (PLS) method can have better performance with small number of calibration set. However, with increasing size of data set as in cross validation method, RBF and BP have better performance. With optimal spreading factor, RBF can have the same degree of accuracy but significantly faster convergent speed comparing to BP. Normalization scheme can also significantly affect the performance of both RBF and BP.

原文English
頁面59-60
頁數2
DOIs
出版狀態Published - 1 一月 1998
事件Proceedings of the 1998 2nd International Conference on Bioelectromagnetism - Melbourne, Australia
持續時間: 15 二月 199818 二月 1998

Conference

ConferenceProceedings of the 1998 2nd International Conference on Bioelectromagnetism
城市Melbourne, Australia
期間15/02/9818/02/98

指紋

深入研究「Quantitative multivariate analysis with artificial neural networks」主題。共同形成了獨特的指紋。

引用此