Dissipative stability theory for linear repetitive processes with application in iterative learning control [PDF]
, 2009 This paper develops a new set of necessary and sufficient conditionsfor the stability of linear repetitiveprocesses, based on a dissipative setting for analysis. Theseconditions reduce the problem of determining whether a linearrepetitive process is stable or not to that of checking for theexistence of a solution to a set of linear matrixinequalities ...
Paszke, Wojuech +3 more
openaire +5 more sources
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 ...
Liu D, He P, Yan H.
europepmc +2 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Stable approach based diagonal recurrent quantum neural networks for identification of nonlinear systems. [PDF]
Sci RepIdentification of nonlinear dynamics from input-output data is crucial in many fields where conventional linear models fail to capture nonlinear dynamics of complex systems.
Khalil H, Elshazly O, Shaheen O.
europepmc +2 more sources
A critical examination of compound stability predictions from machine-learned formation energies [PDF]
, 2020 Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT ...
Bartel, CJ +5 more
core +3 more sources
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
doaj +1 more source
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
core +1 more source