Abstract: The transducer calibration is a key link in engineering test tasks and has significant influence on accuracy and reliability of test results. When input-output relationships of transducers contain unknown and complex nonlinear characteristics, the conventional calibration method can hardly achieve satisfactory accuracy. A neural network (NN) hybrid modeling approach is proposed and applied to transducer calibration. The modeling procedures are also presented in detail. The simulated studies on the calibration of single output and multiple output transducers are conducted respectively by use of the developed hybrid modeling scheme. The NN hybrid modeling approach is utilized to calibrate a six-dimensional force sensor prototype based on the measured data obtained from calibration tests. The simulated and experimental results show that the NN hybrid modeling approach can improve significantly calibration precision in comparison with traditional calibration methods. In addition, the NN hybrid modeling is superior to NN black box modeling because the former possesses smaller network scale, higher convergence speed, higher calibration precision and better generalization performance.

Key words: Calibration, Hybrid modeling, Neural network, Transducer