DocumentCode :
569719
Title :
Application and Design of the Spectral Identification System Based on Neural Network and Fuzzy Control
Author :
Chen, Min ; Liu, Hui
Author_Institution :
Dept. of Comput. Sci. & Technol., Hunan Inst. of Technol., Hengyang, China
fYear :
2012
fDate :
17-19 Aug. 2012
Firstpage :
1143
Lastpage :
1146
Abstract :
The spectral identification technology is a spectral basis of qualitative analysis. With the development of pattern recognition, spectral identification technology has become an important tool for rapid detection of medicine, environmental protection, petrochemical and other industries. The neural network nonlinear mapping, adaptive learning, robustness and fault tolerance features, has a wide range of applications in signal processing, knowledge engineering, pattern recognition and other fields. This paper meets the Lambert Beer law of spectral signals for the study, outlines the basic principles of neural networks for pattern recognition, and then according to the specific requirements of the spectrum recognition, multi-feature-based and neural network spectral identification programs, and conducts system design, the establishment of the basic model framework. Finally, an instance of the method is described.
Keywords :
fuzzy control; neural nets; pattern recognition; spectral analysis; spectroscopy computing; Lambert Beer law; adaptive learning; environmental protection; fault tolerance features; fuzzy control; knowledge engineering; medicine; neural network nonlinear mapping; pattern recognition; petrochemical industries; qualitative analysis; robustness; signal processing; spectral identification system; spectral signals; spectrum recognition; Biological neural networks; Noise; Pattern recognition; Standards; Training; Wavelet domain; multi-feature; neural network; noise variance; spectroscopy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-2406-9
Type :
conf
DOI :
10.1109/ICCIS.2012.77
Filename :
6301316
Link To Document :
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