Huawei Wu1,2 and Yuanjinn Zhang This email address is being protected from spambots. You need JavaScript enabled to view it.1,2

1Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P.R. China
2School of Automotive and Traffic Engineering, Hubei University of Arts and Science, Xiangyang 441053, P.R. China


Received: May 24, 2018
Accepted: May 8, 2019
Publication Date: September 1, 2019

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In view of the nonlinear property of aircraft braking process and the complexity of on-line estimation, a prediction method of optimal slip rate based on improved firefly BP neural network (IFABP) is proposed. By improving the cross factor of the firefly algorithm, the operation speed of the firefly algorithm is enhanced. The global optimization ability of firefly algorithm is used to optimize the weights and thresholds of BP neural network, and the prediction ability of BP neural network is improved.TheimprovedBPneuralnetworkpredictionmodeloffireflyisconstructedbasedontheslip rate-coefficient data under different working conditions, and the optimal slip rate identification system is constructed. The simulation results in the aircraft brake system verify the feasibility and effectiveness of the proposed optimal slip rate identification method.

Keywords: Prediction of Aircraft Optimal Slip Rate, BP Neural Network, Improved Firefly Algorithm


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