TY - GEN
T1 - Fault diagnosis method based on improved genetic algorithm and neural network
AU - Zhang, Dawei
AU - Li, Weilin
AU - Wu, Xiaohua
AU - Lv, Xiaofeng
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - In order to overcome the shortcomings such as slow convergence rate and prone to sink into small locality in BP neural network, adaptive genetic algorithm and BP algorithm are combined to take shape a hybrid algorithm to train artificial neural network. In a specific implementation, firstly, an adaptive genetic algorithm is used to perform multi-point genetic optimization on the initial weight space of the neural network, and better search space is located in the solution space. On this basis, local exact search is performed using BP algorithm, ultimately the global optimum is achieved. This algorithm is simulated based on the fault diagnosis of one certain helicopter's airborne electrical control box and one certain flight control box of aircraft autopilot. The simulation conclusions indicate that the algorithm has faster convergence rate and higher diagnostic accuracy.
AB - In order to overcome the shortcomings such as slow convergence rate and prone to sink into small locality in BP neural network, adaptive genetic algorithm and BP algorithm are combined to take shape a hybrid algorithm to train artificial neural network. In a specific implementation, firstly, an adaptive genetic algorithm is used to perform multi-point genetic optimization on the initial weight space of the neural network, and better search space is located in the solution space. On this basis, local exact search is performed using BP algorithm, ultimately the global optimum is achieved. This algorithm is simulated based on the fault diagnosis of one certain helicopter's airborne electrical control box and one certain flight control box of aircraft autopilot. The simulation conclusions indicate that the algorithm has faster convergence rate and higher diagnostic accuracy.
KW - adaptive genetic algorithm
KW - airborne electrical control box
KW - fault diagnosis
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85070279467&partnerID=8YFLogxK
U2 - 10.1109/CIEEC.2018.8745825
DO - 10.1109/CIEEC.2018.8745825
M3 - 会议稿件
AN - SCOPUS:85070279467
T3 - Proceedings of 2018 IEEE 2nd International Electrical and Energy Conference, CIEEC 2018
SP - 643
EP - 647
BT - Proceedings of 2018 IEEE 2nd International Electrical and Energy Conference, CIEEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Electrical and Energy Conference, CIEEC 2018
Y2 - 4 November 2018 through 6 November 2018
ER -