Results 51 to 60 of about 59,820 (274)
Actuators, 2023 Modeling errors and external disturbances have significant impacts on the control accuracy of robotic arm trajectory tracking. To address this issue, this paper proposes a novel method, the neural network terminal sliding mode control (ALSSA-RBFTSM ...
Jianguo Duan +3 more
doaj +1 more source
A novel radial basis function neural network for fault section estimation in transmission network [PDF]
, 2001 In this paper, the application of Radial Basis Function Neural Network (RBF NN) to fault section estimation in power systems is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN.
Bi, TS +4 more
core
Predicting real-time roadside CO and NO2 concentrations using neural networks [PDF]
, 2008 The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data.
Zito, P., Chen, H., Bell, M.C.
core +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
Advanced Intelligent Discovery, EarlyView.This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai +8 more
wiley +1 more source
Using RBF nets in rubber industry process control [PDF]
, 2010 This paper describes the use of a radial basis function (RBF) neural network. It approximates the process parameters for the extrusion of a rubber profile used in tyre production.
Brause, Rüdiger W., Pietruschka, Ulf
core
Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation
Advanced Intelligent Discovery, EarlyView.We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley +1 more source
Deep Learning‐Assisted Design of Mechanical Metamaterials
Advanced Intelligent Discovery, EarlyView.This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +1 more source
Research on location and layout of auto-body sheet metal based on NSGA-Ⅱ and RBF neural network
Journal of Hebei University of Science and Technology, 2019 In order to solve the problem of low efficiency and easy clamping deformation in the location layout design of auto-body sheet metal,alocation layout design method of auto-body sheet metal based on NSGA-Ⅱ and RBF neural network is proposed.With the ...
Peng WANG, Jiachuan XU, Fan CAO, Di LI
doaj +1 more source
Reinforcement Learning using Augmented Neural Networks
, 2018 Neural networks allow Q-learning reinforcement learning agents such as deep Q-networks (DQN) to approximate complex mappings from state spaces to value functions.
Grzes, Marek, Shannon, Jack
core +1 more source
Advanced Intelligent Discovery, EarlyView.A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source