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Design of Fault Detection Observer Based on Hyper Basis Function

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Wen Xin 1 *   Zhang Xingwang 1   Zhu Yaping 2  
文摘 In this paper, we propose the Hyper Basis Function(HBF) neural network on the basis of Radial Basis Function(RBF) neural network. Compared with RBF, HBF neural networks have a more generalized ability with different activation functions. A decision tree algorithm is used to determine the network center. Subsequently, we design an adaptive observer based on HBF neural networks and propose a fault detection and diagnosis method based on the observer for the nonlinear modeling ability of the neural network. Finally, we apply this method to nonlinear systems. The sensitivity and stability of the observer for the failure of the nonlinear systems are proved by simulation, which is beneficial for real-time online fault detection and diagnosis.
来源 Tsinghua Science and Technology ,2015,20(2):200-204 【核心库】
DOI 10.1109/tst.2015.7085633
关键词 observer ; fault detection ; hyper basis function ; neural networks
地址

1. Faculty of Aerospace Engineering, Shenyang Aerospace University, Shenyang, 110136  

2. College Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016

语种 英文
文献类型 研究性论文
ISSN 1007-0214
学科 自动化技术、计算机技术
文献收藏号 CSCD:5414852

参考文献 共 18 共1页

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