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摘要：In this paper, a novel type of neural networks called grey radial basis function network (GRBFN), is proposed The reasons why grey theory is introduced into the RBF neural network are based on two facts. First, the modeling performance will be affected by the randomness inherent in the data when neural network approach is used to the model. That is, poor performance results from large randomness and vice versa. Then, grey accumulated generating operation (AGO), a basis of the grey theory, is reported possessing randomness reduction property. Because of facts, the GRBFN model is presented and expected to have better modeling precision of random drift in dynamically tuned gyroscopes (DTGs). The novel grey RBF network is applied to drift modeling of DTGs. The numerical results of real drift data from a certain type DTG verify the effectiveness of the proposed GRBFN model powerfully The RBF neural network modeling approach is also investigated to provide a comparison with the GRBFN model. Under identical training condition, the GRBFN's training speed has been enhanced greatly.

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