Performance Fuel Efficiency Estimation for Aircraft Climbing Phase Based on Optimized IGNN Model
Aiming at the change of performance fuel efficiency during the climbing phase of the aircraft,which is affected by a variety of uncertain factors,it presents non-linear and random characteristics.A method of performance fuel efficiency estimation based on the optimized inlaid grey neural network(IGNN)is proposed.This method uses the grey theory to weaken the randomness of the original data,and combines the characteristics of BP neural network with strong nonlinear fitting ability,to build the perfor-mance fuel efficiency estimation model based on IGNN in the climbing phase.Mind evolutionary algorithm(MEA)is used to opti-mize the initial weights and thresholds of IGNN,and to solve the adverse effects of random initialization network weights and thresh-olds on model accuracy.Experimental results show that the model has higher estimation accuracy and stability,and can effectively and accurately estimate the performance fuel efficiency for aircraft climbing phase.
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