Nonlinear calibration with genetic optimizing RBF neural network

Wu Wang; Li-Hui Guo; Xiao-bo Jiao

doi:10.1117/12.905833

28 October 2011 Nonlinear calibration with genetic optimizing RBF neural network

Wu Wang, Li-Hui Guo, Xiao-bo Jiao

Proceedings Volume 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization; 82051F (2011) https://doi.org/10.1117/12.905833
Event: 2011 International Conference on Photonics, 3D-imaging, and Visualization, 2011, Guangzhou, China

Abstract

Virtual instrument was widely used in automatic measurement and control system, nonlinear calibration was necessary in the science research and high-precise measurement. Nonlinear calibration method with RBFNN was proposed in this paper for ANN's ability of self-learning and generalization and GA was introduced to optimize its structure and parameters. The structure of RBFNN was created and optimizing algorithm was proposed, the fundamental of nonlinear calibration was introduced. The simulation shows RBFNN with optimized by GA can greatly increase the convergence speed and precision, nonlinear calibration with ANN was feasible and the precision was obviously improved, this method can be used into automatic measure system effectively.

Citation Download Citation

Wu Wang, Li-Hui Guo, and Xiao-bo Jiao "Nonlinear calibration with genetic optimizing RBF neural network", Proc. SPIE 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization, 82051F (28 October 2011); https://doi.org/10.1117/12.905833

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