SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm

LI Haitao; HE Yuzhu; SONG Ping

doi:10.13700/j.bh.1001-5965.2015.0417

Volume 42 Issue 6

Jun. 2016

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Journal of Beijing University of Aeronautics and Astronautics > 2016 > 42(6): 1093-1098.

Wu Hong, Li Qiushi, Guo Ming, et al. Aerodynamic optimization method of transonic fan rotor based on BVF[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(01): 14-17. (in Chinese)

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LI Haitao, HE Yuzhu, SONG Pinget al. SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1093-1098. doi: 10.13700/j.bh.1001-5965.2015.0417(in Chinese)

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SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm

doi: 10.13700/j.bh.1001-5965.2015.0417

School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Received Date: 23 Jun 2015
Publish Date: 20 Jun 2016

Abstract

Abstract

With regard to the lack of the sample of faults in the test of autopilot, a model of fault diagnosis based on support vector machine (SVM) optimized by quantum inspired gravitational search algorithm is put forward. SVM does well in solving the few samples and nonlinear problem, which is suitable for the fault diagnosis of autopilot. To improve the convergence rate and accuracy of parameters optimizing based on gravitational search algorithm (GSA), quantum inspired gravitational search algorithm (QGSA) was applied to optimizing the parameters of SVM. SVM based on QGSA can solve the overfitting and underfitting resulted from the improper parameters. By this way, a model of fault diagnosis with better performance was built. The simulation experiment results show that the accuracy of SVM based on QGSA can achieve 96.530 6% using 50 training samples. However, the accuracy of genetic algorithm (GA)-SVM achieves 92.040 8% and the accuracy of SVM based on GSA achieves 91.632 7%. The simulation experiment results shows that SVM based on QGSA has much better performance than others.
- autopilot,
- quantum inspired gravitational search algorithm (QGSA),
- support vector machine (SVM),
- fault diagnosis,
- parameters optimizing

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References(16)

References

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