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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Design and Implementation of Novel LMI-Based Iterative Learning Robust Nonlinear Controller
Complexity, 2021 An iterative learning robust fault-tolerant control algorithm is proposed for a class of uncertain discrete systems with repeated action with nonlinear and actuator faults. First, by defining an actuator fault coefficient matrix, we convert the iterative
Saleem Riaz +3 more
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Robust Stability of Iterative Learning Control Schemes [PDF]
, 2007 A notion of robust stability is developed for iterative learning control in the context of disturbance attenuation. The size of the unmodelled dynamics is captured via a gap distance, which in turn is related to the standard H2 gap metric, and the ...
French, Mark
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The effects of spatial stability and cue type on spatial learning: Implications for theories of parallel memory systems [PDF]
Cognition, 2021 Some theories of spatial learning predict that associative rules apply under only limited circumstances. For example, learning based on a boundary has been claimed to be immune to cue competition effects because boundary information is the basis for the formation of a cognitive map, whilst landmark learning does not involve cognitive mapping.
Matt Buckley +6 more
openaire +4 more sources
Evolving stochastic learning algorithm based on Tsallis entropic index [PDF]
, 2005 In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks.
Anastasiadis, A.D., Magoulas, George D.
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The Impact of a Construction Play on 5- to 6-Year-Old Children’s Reasoning About Stability
Frontiers in Psychology, 2020 TheoryYoung children have an understanding of basic science concepts such as stability, yet their theoretical assumptions are often not concerned with stability.
Anke Maria Weber +2 more
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