Simultaneous measurement of temperature and strain: an artificial neural network approach

In this letter, we report the use of an artificial neural network approach for simultaneous recovery of information about strain and temperature from fiber optic sensors. Simulation results show that, for a particular sensor with large cross-sensitivity, temperature and strain measurement accuracy can be increased by 12 and 3 times, respectively, when compared with the matrix inversion method.