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
Probability Based Stochastic Iterative Learning Control for Batch Processes With Actuator Faults
IEEE Access, 2019 This paper proposes a new stochastic composite iterative learning control for batch processes with actuator faults that happen with a certain kind of probability.
Limin Wang, Bingyun Li
doaj +1 more source
Stability theory of game-theoretic group feature explanations for machine learning models
, 2021 82 pages, 43 figures. Typos fixed.
Miroshnikov, Alexey +3 more
openaire +2 more sources
An Actor-Critic Framework for Online Control With Environment Stability Guarantee
IEEE Access, 2023 Online actor-critic reinforcement learning is concerned with training an agent on-the-fly via dynamic interaction with the environment. Due to the specifics of the application, it is not generally possible to perform long pre-training, as it is commonly ...
Pavel Osinenko +3 more
doaj +1 more source
A geometrical analysis of global stability in trained feedback networks [PDF]
, 2019 Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning due to their ability to implement complex computations. While substantial progress in designing effective learning algorithms has been achieved in
Mastrogiuseppe, Francesca +1 more
core +2 more sources
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
openaire +1 more source
BACKPROPAGATION TRAINING ALGORITHM WITH ADAPTIVE PARAMETERS TO SOLVE DIGITAL PROBLEMS [PDF]
ICTACT Journal on Soft Computing, 2011 An efficient technique namely Backpropagation training with adaptive parameters using Lyapunov Stability Theory for training single hidden layer feed forward network is proposed.
R. Saraswathi
doaj
Dynamic ILC for Linear Repetitive Processes Based on Different Relative Degrees
Mathematics, 2022 The current research on iterative learning control focuses on the condition where the system relative degree is equal to 1, while the condition where the system relative degree is equal to 0 or greater than 1 is not considered.
Lei Wang +3 more
doaj +1 more source
Identifying an efficient, thermally robust inorganic phosphor host via machine learning
Nature Communications, 2018 Identifying phosphors with good thermal stability and quantum efficiency is a prerequisite to improve the performance of white LED light sources. Here, a combined machine learning and density functional theory method is introduced to identify next ...
Ya Zhuo +4 more
doaj +1 more source
Predicting the thermodynamic stability of perovskite oxides using machine learning models
, 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
core +1 more source