(Long Short-Term Memory,LSTM)模型收敛速度慢和预测精度低的问题,采用了金枪鱼优化算法(Tuna Swarm Optimization,TSO),利用算法优化LSTM网络模型的超参数,从而对模型的收敛性能和预测性能实现提升.比较TSO-LSTM和传统LSTM,并将两种模型的预测结果与实际航行轨迹进行比较,结果表明基于TSO-LSTM的船舶轨迹预测模型具有更高...
此外,TSO-LSTM模型还能够更好地捕捉时间序列数据中的长期依赖性,提高了预测的准确性和稳定性。 综上所述,基于金枪鱼算法优化的长短时记忆TSO-LSTM模型是一种有效的时序时间序列数据预测方法。通过引入金枪鱼算法优化LSTM模型的参数,TSO-LSTM模型能够更好地捕捉时间序列数据中的长期依赖性,提高预测的准确性和稳定性。未...
Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed...