Design of fault detection observer based on Hyper Basis Function

被引：0
作者：
Faculty of Aerospace Engineering, Shenyang Aerospace University, 110136, China [1 ]
不详 [2 ]
机构：
[1] Faculty of Aerospace Engineering, Shenyang Aerospace University
[2] College Astronautics, Nanjing University of Aeronautics and Astronautics
来源：
Tsinghua Sci. Tech. | / 2卷 / 200-204期
关键词：
fault detection; hyper basis function; neural networks; observer;
D O I：
10.1109/tst.2015.7085633
中图分类号：
学科分类号：
摘要：
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. © 1996-2012 Tsinghua University Press.
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页码：200 / 204
页数：4
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