Results 1 to 10 of about 749 (96)
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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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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International Journal of Analysis and Applications, 2023 The psychological learning theory (PLT) in the formation of moral verdict is represented by the choice-practice paradigm. It involves weighing the effects of various options and choosing one to put into practice.
Doha A. Kattan, Hasanen A. Hammad
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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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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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Designing stable neural identifier based on Lyapunov method [PDF]
Journal of Artificial Intelligence and Data Mining, 2015 The stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks.
F. Alibakhshi +3 more
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COVID-19: Data-Driven Mean-Field-Type Game Perspective
Games, 2020 In this article, a class of mean-field-type games with discrete-continuous state spaces is considered. We establish Bellman systems which provide sufficiency conditions for mean-field-type equilibria in state-and-mean-field-type feedback form.
Hamidou Tembine
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Towards a Unified Theory of Learning and Information
Entropy, 2020 In this paper, we introduce the notion of “learning capacity” for algorithms that learn from data, which is analogous to the Shannon channel capacity for communication systems.
Ibrahim Alabdulmohsin
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Machine learned synthesizability predictions aided by density functional theory
Communications Materials, 2022 In data-driven approaches for materials discovery, it is essential to account for phase stability when predicting synthesizability. Here, by combining density functional theory calculations and machine learning, the authors predict the synthesizability ...
Andrew Lee +6 more
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PD-Type Iterative Learning Control for Uncertain Spatially Interconnected Systems
Mathematics, 2020 This paper puts forward a PD-type iterative learning control algorithm for a class of discrete spatially interconnected systems with unstructured uncertainty.
Longhui Zhou +4 more
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