Aero-optic imaging deviation prediction based on ISSA-ELM

Abstract

When the aircraft is moving at high speed in the atmosphere, aero-optical imaging deviation will appear due to the influence of aero-optical effect. In order to achieve real-time compensation during the flight of the aircraft, it is necessary to analyze and predict the obtained imaging deviation data. In order to improve the search speed and accuracy of the prediction algorithm and the ability to jump out of local optimum, in this paper, an improved sparrow search algorithm optimized extreme learning machine (ISSA-ELM) neural network model is proposed to predict the aero-optical imagine deviation. Finally, the performance of ISSA-ELM, ELM neural network and SSA-ELM neural network was tested. The results showed that compared with ELM and SSA-ELM algorithms, the convergence speed of ISSA-ELM was significantly enhanced, and the accuracy of data prediction was also significantly improved.

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