1992
DOI: 10.1366/0003702924124619
|View full text |Cite
|
Sign up to set email alerts
|

Neural Network System for the Identification of Infrared Spectra

Abstract: A neural network system has been developed on a personal computer to identify 1129 infrared spectra. The system is composed of two steps of networks. The first step classifies 1129 spectra into 40 categories, and each unit of the output layer is connected to one of the 40 networks in the second step, which identify each spectrum. Each network is composed of three layers. The input, intermediate, and output layers are composed of 250, 40, and 40 units, respectively. Intensity data at 250 wavenumber points betwe… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4

Citation Types

0
16
0
1

Year Published

1994
1994
2003
2003

Publication Types

Select...
6
4

Relationship

0
10

Authors

Journals

citations
Cited by 49 publications
(17 citation statements)
references
References 8 publications
0
16
0
1
Order By: Relevance
“…recognition. 17,18 However, sigmoidal transfer functions were selected for calibration in this study because relative abundance, the output to be calculated here, is between 0 and 1, which is the range of output of the sigmoidal function, One of the main objectives of this study was to investigate the performance of ANN with sigmoidal functions for output nodes in calibration problems and the possibility to exploit its ability of pattern recognition at the same time. Although a comparison of ANN and PLS has been previously investigated, 20 there have been few studies that focused on this point.…”
Section: Introductionmentioning
confidence: 99%
“…recognition. 17,18 However, sigmoidal transfer functions were selected for calibration in this study because relative abundance, the output to be calculated here, is between 0 and 1, which is the range of output of the sigmoidal function, One of the main objectives of this study was to investigate the performance of ANN with sigmoidal functions for output nodes in calibration problems and the possibility to exploit its ability of pattern recognition at the same time. Although a comparison of ANN and PLS has been previously investigated, 20 there have been few studies that focused on this point.…”
Section: Introductionmentioning
confidence: 99%
“…Neural networks have emerged as remarkable tools for pattern recognition in scientific applications. They have been applied with good success to spectroscopic problems in nuclear magnetic resonance (8 -10), circular dichroism (11)(12)(13), and infrared spectroscopy (14).…”
Section: Introductionmentioning
confidence: 99%
“…Tanabe et al 8 developed a neural network system to identify unknown compounds from their infrared spectral data by comparing matching indices with a large-scale infrared spectral database. The classification scheme was divided into two tasks.…”
Section: Introductionmentioning
confidence: 99%