Suchergebnisse - Control methods Deep Learning

TorchQC - A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control von Koutromanos, Dimitris, Stefanatos, Dionisis, Paspalakis, Emmanuel

“… The need for a framework that brings together machine learning models and quantum simulation methods has been quite high within the quantum control field, with the ultimate goal of exploiting …”
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Adaptive blind control using deep learning methods von Derbas, Ghadeer, Xhonneux, André, Müller, Dirk

“… This paper presents a data-driven approach to predict shade position using deep learning, aiming to improve the adaptability and occupant satisfaction of automated shading …”
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ED-DQN: An event-driven deep reinforcement learning control method for multi-zone residential buildings von Fu, Qiming, Li, Zhu, Ding, Zhengkai, Chen, Jianping, Luo, Jun, Wang, Yunzhe, Lu, You

“… ) has been adopted to tackle this issue, but traditional RL methods require massive training data, long learning periods, and frequent equipment adjustments …”
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Withdrawal: He, Y., Zhang, J., Bio‐Inspired Algorithms For Industrial Robot Control Using A Deep Learning Method. Agronomy Journal. 2022; Accepted Articles. https://doi.org/10.1002/agj2.21214

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Dynamic speed trajectory generation and tracking control for autonomous driving of intelligent high-speed trains combining with deep learning and backstepping control methods von Wang, Xi, Li, Shukai, Cao, Yuan, Xin, Tianpeng, Yang, Lixing

“… learning and backstepping control methods. By exploiting the deep learning methods, a speed trajectory generator is trained with the actual driving data …”
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An Optimal Control Deep Learning Method to Design Artificial Viscosities for Discontinuous Galerkin Schemes von Bois, Léo, Franck, Emmanuel, Navoret, Laurent, Vigon, Vincent

“… In this paper, we propose a method for constructing a neural network viscosity in order to reduce the non-physical oscillations generated by high-order Discontinuous Galerkin methods on uniform Cartesian grids …”
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LSTM-MPC: A Deep Learning Based Predictive Control Method for Multimode Process Control von Huang, Keke, Wei, Ke, Li, Fanbiao, Yang, Chunhua, Gui, Weihua

“… Inspired by the powerful representation capabilities of deep learning, this paper proposed a deep learning based MPC method …”
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A deep reinforcement learning control method for multi-zone precooling in commercial buildings von Fan, Yuankang, Fu, Qiming, Chen, Jianping, Wang, Yunzhe, Lu, You, Liu, Ke

“… •By combining precooling control with the Deep Q-Network algorithm, the method effectively handles complex environmental changes, significantly improving precooling performance and showing strong energy-saving potential …”
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A deep reinforcement learning control method for PEMFC thermal management and air supply system von Li, Wenhao, Li, Shuai, Du, Changqing, Xu, Yinsong, Xin, Qianqian, Yan, Fuwu

“… , especially under dynamic load conditions. This study proposes a new control strategy based on deep reinforcement learning (DRL …”
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An immune optimization deep reinforcement learning control method used for magnetorheological elastomer vibration absorber von Wang, Chi, Cheng, Weiheng, Zhang, Hongli, Dou, Wei, Chen, Jinbo

“… (PID), fuzzy control, and ON-OFF algorithms to control the vibration absorption system …”
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Effective MPC strategies using deep learning methods for control of nonlinear system von Rajasekhar, N., Nagappan, K. Kumaran, Radhakrishnan, T. K., Samsudeen, N.

“… With the advent of sophisticated deep learning methods, neural networks can be employed to improve the computational efficiency of the MPC …”
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A multi-channel active noise control system using deep learning-based method to estimate secondary path and normalized-clustered control strategy for vehicle interior engine noise von Cheng, Can, Liu, Zhien, Chen, Wan, Li, Xiaolong, Liao, Wu, Lu, Chihua

“… •A deep learning method is proposed to estimate the secondary paths, which avoids the frequent re-estimation of secondary paths using the traditional offline estimation method under the disturbance …”
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An anti-swing control method combining deep learning prediction models with a multistate fractional-order terminal sliding mode controller for wave motion compensation devices von Wang, Yao, Lu, Xinrui, Gao, Yuantian, Chen, Yuan

“… Therefore, an anti-swing control method is proposed that combines deep learning prediction models with a multistate fractional-order terminal sliding mode controller for wave motion compensation devices …”
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Optimal energy management for air cooled server fans using Deep Reinforcement Learning control method von Fulpagare, Yogesh, Huang, Kuei-Ru, Liao, Ying-Hao, Wang, Chi-Chuan

“… •The design of reward function has a major influence on the control actions. The current study proposed the Deep Reinforcement Learning (DRL …”
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A deep reinforcement learning method to control chaos synchronization between two identical chaotic systems von Cheng, Haoxin, Li, Haihong, Dai, Qionglin, Yang, Junzhong

“… We propose a model-free deep reinforcement learning method for controlling the synchronization between two identical chaotic systems, one target and one reference …”
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Digital twin enabled real-time advanced control of TBM operation using deep learning methods von Zhang, Limao, Guo, Jing, Fu, Xianlei, Tiong, Robert Lee Kong, Zhang, Penghui

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Deep Learning Methods for Mean Field Control Problems With Delay von Fouque, Jean-Pierre, Zhang, Zhaoyu

“… Two numerical algorithms are provided based on deep learning techniques, one is to directly parameterize the optimal control using neural networks, the other is based on numerically solving the McKean …”
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Time series imaging-based deep learning method for inventory control von Tian, Yu-Xin, Zhang, Chuan

“… To remedy this, we propose a time series imaging-based deep learning method, which automatically extracts crucial features alongside those selected manually …”
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Bio-inspired algorithms for industrial robot control using deep learning methods von Guan, Jiwen, Su, Yanzhao, Su, Ling, Sivaparthipan, C.B., Muthu, BalaAnand

“… Hence, in this study, a Bio-inspired Intelligent Industrial Robot Control System (BIIRCS) has been suggested using Deep Learning methods …”
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A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks von Mao, Bomin, Tang, Fengxiao, Fadlullah, Zubair Md, Kato, Nei, Akashi, Osamu, Inoue, Takeru, Mizutani, Kimihiro

“… we propose a non-supervised deep learning based routing strategy running in the SDN controller …”
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