Modified Particle Swarm Optimization Based Algorithm for BP Neural Network for Measuring Aircraft Remaining Fuel Volume

摘要：

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.

关键词： Remaining Fuel of Aircraft Improved PSO BP Neural network Optimize Weight Adjustment

作者: GAO Na QU Zhi-hong

作者单位: Xian Railway Vocational & Technical Institute, Xian, Shanxi 710014, China Engineering College, Air Force Engineering University, Xi’an Shanxi 710038, China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 3398-3401

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）