Title :
Spectroscopy and hybrid neural network analysis
Author :
Lu, Taiwei ; Lerner, Jeremy
Author_Institution :
Physical Opt. Corp., Torrance, CA, USA
fDate :
6/1/1996 12:00:00 AM
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;
Journal_Title :
Proceedings of the IEEE
