Abstract:

In order to accurately reflect the digital input and output characteristics of eddy current sensors and to improve traditional BP neural networks, LMBP（Levenberg Marquart Back Propagation） neural networks and RBF (Radial Basis Function) neural networks are first constructed. Then the two types of neural networks are applied respectively to the characteristic curve fitting of ECS（Eddy Current Sensors）. Finally a comparison is made to compare the fitting results of the two networks. The simulation results show that with the same number of training samples, the networks are small or medium sized, compared with LMBP, RBF neural networks are superior in fitting error, convergence speed and fitting precision. And this provides a basis for the choice of fitting method of one-dimensional data in practical engineering.

Key words: levenberg-marquart back propagation (LMBP) neural network, radial basis function (RBF) neural network, eddy current sensor, curve fitting