Title :
Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements
Author :
Pardo, Antonio ; Marco, Santiago ; Samitier, Josep
Author_Institution :
Dept. d´´Electron., Barcelona Univ., Spain
fDate :
6/1/1998 12:00:00 AM
Abstract :
Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy
Keywords :
array signal processing; arrays; gas sensors; identification; measurement errors; neural nets; series (mathematics); DSP; Wiener series; artificial neural networks; chemical sensor arrays; digital signal processing; gas sensing systems; identification techniques; low-cost sensor arrays; measurement accuracy; multicomponent gas mixtures; nonlinear inverse dynamic models; nonlinearities; quantitative measurements; slow time-response; Artificial neural networks; Chemical sensors; Digital signal processing; Gain measurement; Gas detectors; Gases; Inverse problems; Sensor arrays; Sensor systems; Time measurement;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
