Modified particle swarm optimization based algorithm for BP neural network for measuring aircraft remaining fuel volume

Aimed at the problem that when fuel level of the aircraft in the flight, is rise and fall because of tanks' vibration, which result in that calculate model of static condition produces bigger measurement error. BP neural network algorithm is put forward to calculate the remaining fuel of the ai...

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Veröffentlicht in:Proceedings of the 31st Chinese Control Conference S. 3398 - 3401
Hauptverfasser: Gao Na, Qu Zhi-Hong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2012
Schlagworte:
Aircraft
Biological neural networks
BP Neural network
Convergence
Fuels
Improved PSO
Mathematical model
Neurons
Optimize
Particle swarm optimization
Remaining Fuel of Aircraft
Weight Adjustment
ISBN:1467325813, 9781467325813
ISSN:1934-1768
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Zusammenfassung:Aimed at the problem that when fuel level of the aircraft in the flight, is rise and fall because of tanks' vibration, which result in that calculate model of static condition produces bigger measurement error. BP neural network algorithm is put forward to calculate the remaining fuel of the airplane. However, because BP neural network has the limitations, which are lower learning efficiency, slow convergence and the local extreme values, a kind of improved PSO algorithm is adopted to optimize the training of the BP neural network. Then, we apply the PSO-BP algorithm to measure the aircraft remaining fuel volume. Finally, the results of experiments indicate that compared with the traditional BP algorithm, the PSO-BP algorithm has advantages of lower training time, lower relative error and higher control accuracy, and it also can enhance the measurement accuracy of the fuel volume.
ISBN:1467325813
9781467325813
ISSN:1934-1768