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This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method,the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However,the maximum nonlinearity error is 0.075% using the least square method.
This paper presents a method used to the numeral eddy current sensor modeling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. so the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. the nonlinear model has the advantages of strong robustness, on-line modeling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. Even, the maximum nonlinearity error is 0.075% using the least square method.