Showing 81 - 100 results of 292 for search '(((fast OR east) OR fact) OR face) (search OR research) random three algorithm', query time: 0.28s Refine Results
  1. 81

    Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task. by ZeSheng Lin

    Published 2025-01-01
    “…Secondly, the WOA's position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. …”
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    Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data by Jiali Wang, Jia Chen

    Published 2024-12-01
    “…Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). …”
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    Three-Dimensional Trajectory Tracking Control for Stratospheric Airship Based on Deep Reinforcement Learning by Xixiang Yang, Fangchao Bai, Xiaowei Yang, Yuelong Pan

    Published 2025-01-01
    “…The Boltzmann random distribution of reward value and probability of wind direction angle were taken as the action selection criteria of the Q-learning algorithm, the cerebellar model articulation controller (CMAC) neural network was constructed for the discrete action value, and the optimal action sequence was fast obtained. …”
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    Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm by Di Zheng, Baobao Zheng

    Published 2025-04-01
    “…To deal with the optimization model, the particle swarm optimization is adopted and improved in three aspects. These three improvements include randomly updating inertia weights, introducing acceleration factors to replace learning factors, and introducing fast non-dominated sorting for better or worse selection, and improving the optimization ability of the algorithm by solving the crowding distance. …”
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