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- W4367016590 abstract "In this study, a PSO-LSTM model is proposed <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>[1]</sup> to increase the accuracy of rail curve detection. In order to alter the model's parameters, Particle Swarm Optimization (PSO) techniques are used to enhance the hidden layer and learning rate of the long-and-short-term memory neural network (LSTM). Afterward, apply the PSO-LSTM model to determine the rail's curvature. To make up the identifying process: Data processing, PSO algorithm optimization, and PSO-LSTM model prediction are the three steps. The PSO-LSTM <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>[2]</sup> model is validated using data from train tracks, and it is contrasted with the Back Propagation(Bp) <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>[3]</sup> and conventional LSTM <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>[4]</sup> models. The outcomes demonstrate the superior accuracy and stability of the PSO-LSTM model." @default.
- W4367016590 created "2023-04-27" @default.
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- W4367016590 date "2023-02-24" @default.
- W4367016590 modified "2023-09-25" @default.
- W4367016590 title "Rail track curve recognition model based on PSO-LSTM algorithm" @default.
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- W4367016590 doi "https://doi.org/10.1109/nnice58320.2023.10105699" @default.
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