Results 61 to 70 of about 54,415 (225)
Enabling Stochastic Dynamic Games for Robotic Swarms
Advanced Intelligent Systems, EarlyView.This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
Do optimization methods in deep learning applications matter? [PDF]
, 2020 With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention.
Kiran, Mariam, Ozyildirim, Buse Melis
core +1 more source
Asia-Pacific Journal of Chemical Engineering, EarlyView.ABSTRACT In this study, the actual route of methylene blue (MB) dye adsorption by using fabricated polyfunctional activated carbon–copper oxide nanowires (AC@CuO‐NWs) from bulky wastewater bodies has been investigated. To better understand the exact pathway of the adsorption process, a prominent statistical physics formalism or grand canonical ...
Abdellatif Sakly +7 more
wiley +1 more source
The Canadian Journal of Chemical Engineering, EarlyView.Abstract Global energy demand and environmental concerns have intensified the search for renewable and sustainable energy sources. This study thus, focuses on optimizing the transesterification process of waste cooking oil (WCO) using thermally activated basic oxygen furnace slag catalyst calcined at 850°C (BOF 850). The optimization and modelling were
Johra S. Ali, Hillary L. Rutto
wiley +1 more source
Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction
Discrete Dynamics in Nature and Society, 2014 For decades, Mackey-Glass chaotic time series prediction has attracted more and more attention. When the multilayer perceptron is used to predict the Mackey-Glass chaotic time series, what we should do is to minimize the loss function.
Junsheng Zhao +3 more
doaj +1 more source
Journal of Applied Sciences and Environmental Management, 2019 This paper presents diagnostic and prognostic analysis of oil and gas pipeline industries with allowable corrosion rate using artificial neural networks approach.
M. Obaseki
doaj +1 more source
, 2018 Neural network models for dynamic systems can be trained either in parallel or in series-parallel configurations. Influenced by early arguments, several papers justify the choice of series-parallel rather than parallel configuration claiming it has a ...
Aguirre, Luis A., Ribeiro, Antônio H.
core +1 more source
Energy Science &Engineering, EarlyView.On the basis of core and log data, a Bayesian‐Optimized Random Forest model achieved 92.76% accuracy in classifying tight sandstone reservoirs. A gray relational analysis‐derived evaluation index shows > 80% consistency with actual gas zones. ABSTRACT Tight sandstone gas (TSG), an unconventional oil–gas resource, has heterogeneous reservoirs ...
Yin Yuan +8 more
wiley +1 more source
Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors [PDF]
Geoscientific Instrumentation, Methods and Data Systems, 2018 A new modified elementary Levenberg–Marquardt Algorithm (M-LMA) was used to minimise backpropagation errors in training a backpropagation neural network (BPNN) to predict the records related to the Chi-Chi earthquake from four seismic stations ...
J.-W. Lin, C.-T. Chao, J.-S. Chiou
doaj +1 more source
Frontiers in Public Health, 2022 PurposeThis study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study.MethodsThe ...
H. O. Tekin +10 more
doaj +1 more source