Grey Neural Network Modeling for Machine Tool Thermal Error

This paper proposed a new model of prediction on thermal error of machine tools based on grey neural network combining the data processing merits of grey model and artificial neural network,respectively.The new model can be classified into two forms——parallel grey neural network(PGNN) and inlaid grey neural network(IGNN).The former is to predict the thermal error with optimal linear combination of the result from grey model and artificial neural network respectively,while the weight value of this model is subject to the required accuracy of the experiment.The latter is to optimize the topological structure of the neural network by adding a grey layer before the input layer and a white layer after the output layer,so as to reduce the randomness of the original data and enhance the robustness and the fault-tolerant ability.Compared with the traditional grey model and the artificial neural model,the two forms of grey neural network model prove better in terms of prediction accuracy,calculation convenience and robustness.What's more,they require less to the original data.Thus,the new proposed models are recommended to be applied to different working environment to compensate the thermal error of machine tools.