LSTM Network Based on on Antlion Optimization and its Application in Flight Trajectory Prediction

With the rapid development of intelligent algorithm theory, the application of intelligent algorithms to optimize the structure of network has become a trend. Combining with the advantages of Antlion optimization (ALO), this paper proposes to use ALO algorithm to optimize the initial weights and thresholds of LSTM network, which improves the convergence speed and accuracy, and prevents the algorithm from falling into the local optimal value, perfecting the performance of LSTM networks. Experimental results of trajectory prediction show that compared with BP neural network and LSTM neural network, ALO-LSTM trajectory prediction model has better prediction ability and can predict the trajectory quickly and accurately, ensuring the accuracy timeliness of trajectory prediction.