Artificial Neural Network in the Autopilot System Application of Troubleshooting

Abstract:

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the existing fault diagnosis system in fault detection aspects of Boeing 737 A/P is effective, but in fault isolation aspects performance is poor, therefore using ANN technology need to improve its diagnosis system. A/P for the typical fault, the three layers feed forward artificial neural network structure, this paper introduces the conjugate gradient BP algorithm and gives the diagnosis results. Diagnosis results show that artificial neural network can accurately identify system three typical faults, improve the efficiency of fault diagnosis and fault isolation capability.

Info:

Periodical:

Edited by:

Jun Zhang, Zhijian Wang, Shuren Zhu and Xiaoming Meng

Pages:

3198-3202

Citation:

P. Zhang and S. C. Zhang, "Artificial Neural Network in the Autopilot System Application of Troubleshooting", Applied Mechanics and Materials, Vols. 263-266, pp. 3198-3202, 2013

Online since:

December 2012

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$38.00

* - Corresponding Author

[1] Xiaoyun Shan, Liming Chen. Conjugate gradient method optimized BP neural network coke quality prediction model, edited by china university of mining &technology (Beijing) science (in Chinese).

[2] Shoubing Chen. About the conjugate gradient method, (in Chinese).

[3] Qiuyan Wang, Jianbo Yu. Optimized BP neural network in the application of electronic equipment fault diagnosis, edited by naval aviation engineering institute (in Chinese).

[4] 737-800 Chinese AMM manual.

[5] Zhaoyang Chen, Xiaoshuai Xing. Conjugate gradient BP algorithm in realization of Matlab7. 0, edited by shanxi normal university (in Chinese).

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