Synonyme wurden verwendet für: learning
Suche ohne Synonyme: keywords:(learning)

1–20 von 36 Ergebnissen
|

    Iterative Learning Control Algorithm for Feedforward Controller of EGR and VGT Systems in a CRDI Diesel Engine

    Min, Kyunghan / Sunwoo, Myoungho / Han, Manbae | Springer Verlag | 2018
    Schlagwörter: Learning control , Iterative learning control

    Decision-making for Connected and Automated Vehicles in Chanllenging Traffic Conditions Using Imitation and Deep Reinforcement Learning

    Hu, Jinchao / Li, Xu / Hu, Weiming et al. | Springer Verlag | 2023
    Schlagwörter: Imitation learning , Deep reinforcement learning

    Pedestrian Collision Avoidance Using Deep Reinforcement Learning

    Rafiei, Alireza / Fasakhodi, Amirhossein Oliaei / Hajati, Farshid | Springer Verlag | 2022
    Schlagwörter: Deep reinforcement learning , Car Learning to Act (CARLA)

    SHapley Additive exPlanations for Explaining Artificial Neural Network Based Mode Choice Models

    Koushik, Anil / Manoj, M. / Nezamuddin, N. | Springer Verlag | 2024
    Schlagwörter: Deep learning , Machine learning

    Trajectory Planning for Automated Parking Systems Using Deep Reinforcement Learning

    Du, Zhuo / Miao, Qiheng / Zong, Changfu | Springer Verlag | 2020
    Schlagwörter: Deep reinforcement learning

    Driving cycle recognition neural network algorithm based on the sliding time window for hybrid electric vehicles

    Wang, J. / Wang, Q. N. / Zeng, X. H. et al. | Springer Verlag | 2015
    Schlagwörter: Learning vector quantization

    Application of iterative learning control in tracking a Dubin’s path in parallel parking

    Panomruttanarug, Benjamas | Springer Verlag | 2017
    Schlagwörter: Iterative learning control

    Reinforcement-Tracking: An End-to-End Trajectory Tracking Method Based on Self-Attention Mechanism

    Zhao, Guanglei / Chen, Zihao / Liao, Weiming | Springer Verlag | 2024
    Schlagwörter: Reinforcement learning

    Traffic Flow Forecasting Based on Transformer with Diffusion Graph Attention Network

    Zhang, Hong / Wang, Hongyan / Chen, Linlong et al. | Springer Verlag | 2024
    Schlagwörter: Deep learning

    Road identification in monocular color images using random forest and color correlogram

    Choi, J. H. / Song, G. Y. / Lee, J. W. | Springer Verlag | 2012
    Schlagwörter: Learning paradigm

    Learning To Recognize Driving Patterns For Collectively Characterizing Electric Vehicle Driving Behaviors

    Lee, Chung-Hong / Wu, Chih-Hung | Springer Verlag | 2019
    Schlagwörter: Machine learning

    Algorithm for Detecting the Critical Elements of Airborne Radar Systems Based on Operational Analysis of Diagnostic Information Time Series

    Shevtsov, V. A. / Timoshenko, A. V. / Perlov, A. Yu. et al. | Springer Verlag | 2022
    Schlagwörter: machine learning

    An Autonomous Driving Approach Based on Trajectory Learning Using Deep Neural Networks

    Wang, Dan / Wang, Canye / Wang, Yulong et al. | Springer Verlag | 2021
    Schlagwörter: Trajectory learning

    Drowsy behavior detection based on driving information

    Wang, M. S. / Jeong, N. T. / Kim, K. S. et al. | Springer Verlag | 2016
    Schlagwörter: Ensemble machine learning method

    Development of a Light and Accurate Nox Prediction Model for Diesel Engines Using Machine Learning and Xai Methods

    Park, Jeong Jun / Lee, Sangyul / Shin, Seunghyup et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    High Definition Map Aided Object Detection for Autonomous Driving in Urban Areas

    Endo, Yuki / Javanmardi, Ehsan / Gu, Yanlei et al. | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions

    Boada, M. J. L. / Boada, B. L. / Garcia-Pozuelo, D. et al. | Springer Verlag | 2011
    Schlagwörter: Recursive Lazy learning

    A Review of Optimal Energy Management Strategies Using Machine Learning Techniques for Hybrid Electric Vehicles

    Song, Changhee / Kim, Kiyoung / Sung, Donghwan et al. | Springer Verlag | 2021
    Schlagwörter: Machine learning