extrapolation of calibration curve of hot-wire spirometer using a novel neural network based approach

Hot-wire spirometer is a kind of constant temperature anemometer (cta). the working principle of cta, used for the measurement offluid velocity and flow turbulence, is based on convective heat transfer from a hot?wire sensor to a fluid being measured. the calibration curve of a cta is nonlinear and cannot be easily extrapolated beyond its calibration range. therefore, a method for extrapolation of cta calibration curve will be of great practical application. in this paper, a novel approach based on the conventional neural network and self?organizing map (som) method has been proposed to extrapolate cta calibration curve for measurement of velocity in the range 0.7?30 m/seconds. results show that, using this approach for the extrapolation of the cta calibration curve beyond its upper limit, the standard deviation is about ?0.5%, which is acceptable in most cases. moreover, this approach for the extrapolation of the cta calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometry applications.finally, the standard deviation on the whole measurement range (0.7?30 m/s) is about 1.5%.