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 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.