Deformation Measurement of the Large Flexible Surface by Improved RBFNN Algorithm and BPNN Algorithm

Authors:
Xiyuan Chen
Affiliations:
School of Instrument Science and Engineering, Southeast University, Nanjing City, 210096, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

The Radial Basis Function (RBF) Neural Network (NN) is one of the approaches which has shown a great promise in this sort of problems because of its faster learning capacity. This paper presents the information fusion method based on improved RBFNN to deduce the deformation information of the whole flexible surface considering the complexity of the deformation of the large flexible structure. A distributed Strapdown Inertial Units (SIU) information fusion model for deformation measurement of the large flexible structure is presented. Comparing with the modeling results by improved RBFNN and back propagation (BP) NN, the simulation on a simple thin plate model shows that the information fusion based on improved RBFNN is effective and has higher precision than based on BPNN.