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AERO-ENGINE FULL FLIGHT ENVELOPE MODEL ADAPTIVE MODIFICATION METHOD BASED ON DEEP LEARNING ALGORITHM
AERO-ENGINE FULL FLIGHT ENVELOPE MODEL ADAPTIVE MODIFICATION METHOD BASED ON DEEP LEARNING ALGORITHM
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机译:基于深度学习算法的航空发动机全飞行包络模型自适应修正方法
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摘要
An aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm. A dynamic parallel compensator based on a recursive neural network is adopted to compensate the error of the original nonlinear model within the full flight envelope under the condition without aero-engine performance deterioration. A modifier based on a genetic algorithm is also adopted to conduct adaptive adjustment on correction coefficients of health parameters to be modified in the original nonlinear component-level model. The health parameters to be modified are determined by a multi-attribute decision algorithm based on integrated evaluation. The sum of the modified nonlinear component-level model output and the compensator output is consistent with the aero-engine operation test output data. This provides powerful support for the design of aero-engine control systems and fault diagnosis systems.
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