Evaluation of Goaf Stability Based on Transfer Learning Theory of Artificial Intelligence [PDF]
IEEE Access, 2019 Current artificial intelligence models for evaluating goaf stability in underground metal mines need a large amount of sample data for training, and their accuracy declines with small sample size. With the aim of solving this problem, this paper proposes
Yaguang Qin +4 more
doaj +5 more sources
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview [PDF]
Annual Reviews in Control, 2021 Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution ...
Hiroyasu Tsukamoto +2 more
exaly +7 more sources
Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach [PDF]
IEEE Robotics and Automation Letters, 2021 IEEE Robotics and Automation Letters (RA-L), Preprint Version. Accepted June, 2021 (DOI: 10.1109/LRA.2021.3091019)
Hiroyasu Tsukamoto, Soon-Jo Chung
exaly +6 more sources
Axioms, 2022 We show how to apply the well-known fixed-point approach in the study of the existence, uniqueness, and stability of solutions to some particular types of functional equations. Moreover, we also obtain the Ulam stability result for them.
Ali Turab, Janusz Brzdęk, Wajahat Ali
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Adaptive Predefined-Time Tracking Control for Robotic Manipulator Based on Actor-Critic Reinforcement Learning [PDF]
SensorsThis paper proposes a novel predefined-time adaptive neural tracking control method for uncertain manipulator systems based on Actor-Critic reinforcement learning framework.
Yong Qin, Yuan Sun, Jun Huang, Yankai Li
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Stable approach based diagonal recurrent quantum neural networks for identification of nonlinear systems [PDF]
Scientific ReportsIdentification of nonlinear dynamics from input-output data is crucial in many fields where conventional linear models fail to capture nonlinear dynamics of complex systems.
Hossam Khalil +2 more
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Machine Learning: Science and TechnologyActive learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials.
Yaohuang Huang, Yi-Fan Hou, Pavlo O Dral
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A Comprehensive Survey of Continual Learning: Theory, Method and Application [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime.
Liyuan Wang +3 more
semanticscholar +1 more source
Learning Theory for Dynamical Systems [PDF]
SIAM Journal on Applied Dynamical Systems, 2022 The task of modelling and forecasting a dynamical system is one of the oldest problems, and it remains challenging. Broadly, this task has two subtasks - extracting the full dynamical information from a partial observation; and then explicitly learning ...
Tyrus Berry, Suddhasattwa Das
semanticscholar +1 more source
Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit [PDF]
IEEE Transactions on Power Systems, 2023 Machine learning (ML) for transient stability assessment has gained traction due to the significant increase in computational requirements as renewables connect to power systems.
Robert I. Hamilton, P. Papadopoulos
semanticscholar +1 more source