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
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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
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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
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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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The impact of cognitive schema on learning transfer ability and stability of classical Chinese poetry. [PDF]
PLoS ONEClassical Chinese poetry is a condensed vessel of Chinese culture, bearing the core ethos of history, philosophy, and ethics. The symbolic imagery system in poetry facilitates the construction of cognitive schemata, thereby advancing transfer learning ...
Dawei Liu, Ping He, Huifen Yan
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Actor-Critic Reinforcement Learning for Control with Stability Guarantee
IEEE Robotics and Automation Letters, 2020 Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.
Han, Minghao +3 more
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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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Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning [PDF]
Symmetry, 2021 Probabilistic functional equations have been used to analyze various models in computational biology and learning theory. It is worth noting that they are linked to the symmetry of a system of functional equations’ transformation. Our objective is to propose a generic probabilistic functional equation that can cover most of the mathematical models ...
Ali Turab, Won-Gil Park, Wajahat Ali
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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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Predicting the thermodynamic stability of perovskite oxides using machine learning models
Computational Materials Science, 2018 Perovskite materials have become ubiquitous in many technologically relevant applications, ranging from catalysts in solid oxide fuel cells to light absorbing layers in solar photovoltaics.
Jacobs, Ryan, Li, Wei, Morgan, Dane
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