In this paper,we present a method used to the numeral eddy current sensor modeling based on 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 scaling and high precision.The maximum nonlinearity error can be reduced to 0.037% using GNN.However,the maximum nonlinearity error is 0.075% using least square method (LMS).
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机译：Discussion of 'Maximum Gradient Decision-Making for Railways Based on Convolutional Neural Network' by Hao Pu, Hong Zhang, Paul Schonfeld, Wei Li, Jie Wang, Xianbao Peng, and Jianping Hu
机译：基于优化遗传神经网络的井下运输机械滚动轴承故障诊断（The Application of Optimizing the GENETIC NEURAL NETWORK to the Fault Diagnosis of Rolling Bearings of Transporting Machinery Underground）
