Choice of Optimum Model Parameters in Artificial Neural Networks and Application to X-ray Fluorescence Analysis

Autor:	Changlin Guo, Liqiang Luo, Ang Ji, Guangzu Ma
Rok vydání:	1997
Předmět:	Multivariate statistics Matrix (mathematics) Artificial neural network Calibration (statistics) Computer science Computer Science::Neural and Evolutionary Computation X-ray fluorescence Artificial neural network model Model parameters Biological system Spectroscopy
Zdroj:	X-Ray Spectrometry. 26:15-22
ISSN:	1097-4539 0049-8246
DOI:	10.1002/(sici)1097-4539(199701)26:1<15::aid-xrs182>3.0.co;2-8
Popis:	The model parameters in artificial neural networks have a great influence on the training speed. It can be increased after choosing the optimum parameters, which was performed by a stepping technique. The training speed using the method is usually faster than that when adopting random or empirical parameters. An artificial neural network model was used in multivariate matrix calibration and compared with cross-validation and partial least-squares methods, which were combined with the fundamental-parameters in x-ray fluorescence analysis. The results show that the artificial neural network model produced the highest accuracy.
Databáze:	OpenAIRE
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