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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Evaluation of Goaf Stability Based on Transfer Learning Theory of Artificial Intelligence
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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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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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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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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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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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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