Search alternatives:
significant optimization » significant limitation (Expand Search), significant application (Expand Search), significant association (Expand Search)
main decrease » gain decreased (Expand Search), small decrease (Expand Search), point decrease (Expand Search)
mean decrease » a decrease (Expand Search)
significant optimization » significant limitation (Expand Search), significant application (Expand Search), significant association (Expand Search)
main decrease » gain decreased (Expand Search), small decrease (Expand Search), point decrease (Expand Search)
mean decrease » a decrease (Expand Search)
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1
Snake optimizer algorithm optimization process.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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2
Process of UWB location prediction based on LSTM.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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3
Time of flight ranging model.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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4
Localization trajectory for experiment 2.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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5
Crane experimental platform.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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6
Localization trajectory for experiment 1.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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7
SO-LSTM loss value curve.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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8
UWB data grouping.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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9
Network model and time slot allocation.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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10
SO-LSTM algorithm parameters.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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11
Visualization of ranging error in experiment 1.
Published 2023“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”
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12