Spectroscopy and hybrid neural network analysis

Author

Lu, Taiwei ; Lerner, Jeremy

Author_Institution

Physical Opt. Corp., Torrance, CA, USA

Volume

84

Issue

6

fYear

1996

fDate

6/1/1996 12:00:00 AM

Firstpage

895

Lastpage

905

Abstract

This paper reviews the current use of spectroscopy and related instrumentation in chemical analysis. Advancements in digital signal processing technology are making it possible to improve the sensitivity and accuracy of analytical instruments without expensive upgrading of instrument hardware. A hybrid neural network (HNN) is described that can perform nonlinear signal analysis. The HNN approach combines the simple data reduction capability of conventional linear signal processing algorithms with the adaptive learning and recognition ability of a multilayer nonlinear neural network architecture. A number of examples show the rise of the HNN for environmental monitoring and real-time process control

Keywords

chemical variables measurement; learning (artificial intelligence); monitoring; optical information processing; optical neural nets; pollution measurement; reviews; spectrochemical analysis; adaptive learning; analytical instrument accuracy; chemical analysis; digital signal processing technology; environmental monitoring; hybrid neural network analysis; linear signal processing algorithms; multilayer nonlinear neural network architecture; nonlinear signal analysis; real-time process control; recognition ability; reviews; sensitivity; simple data reduction capability; Chemical analysis; Chemical technology; Digital signal processing; Hardware; Instruments; Multi-layer neural network; Neural networks; Signal analysis; Signal processing algorithms; Spectroscopy;

fLanguage

English

Journal_Title

Proceedings of the IEEE

Publisher

ieee

ISSN

0018-9219

Type

jour

DOI

10.1109/5.503145

Filename

503145