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
Higher-Derivative Corrected Black Holes: Perturbative Stability and Absorption Cross-Section in Heterotic String Theory [PDF]
, 2006 This work addresses spherically symmetric, static black holes in higher-derivative stringy gravity. We focus on the curvature-squared correction to the Einstein-Hilbert action, present in both heterotic and bosonic string theory.
Schiappa, R. +3 more
core +1 more source
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
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Robust Stability of Iterative Learning Control Schemes
, 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 ...
Mark French, French, Mark
core +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
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Stable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems [PDF]
Control and Optimization in Applied Mathematics, 2019 Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated.
Ghasem Ahmadi
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Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source
Mental models vs cell schemes [PDF]
Investigações em Ensino de Ciências, 2002 Student's mental representations of cell are examined from the perspectives of Johnson-Laird's mental models theory five years after instruction. The observed identity and stability of such representations are then interpreted under the framework of ...
Mª Luz Rodríguez Palmero +1 more
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Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Differential Privacy in Federated Learning: An Evolutionary Game Analysis
Applied SciencesThis paper examines federated learning, a decentralized machine learning paradigm, focusing on privacy challenges. We introduce differential privacy mechanisms to protect privacy and quantify their impact on global model performance.
Zhengwei Ni, Qi Zhou
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