Nonlinear Calibration with Genetic Optimizing RBF Neural Network

被引：0
|
作者：
Wang Wu [3 ,1 ]
Guo Li-Hui [1 ]
Jiao Xiao-bo [2 ]
机构：
[1] Xu Chang Univ, Sch Elect & Informat Engn, Xuchang, Peoples R China
[2] Xuchang Electr power co, Power telecommunicat Ctr, Xuchang, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON PHOTONICS, 3D-IMAGING, AND VISUALIZATION | 2011年 / 8205卷
关键词：
Nonlinear calibration; rbf neural networks; genetic algorithm; simulation;
D O I：
10.1117/12.905833
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
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.
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页数：5
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