A highly effective, recyclable, and novel host-guest nanocomposite for Triclosan removal: A comprehensive modeling and optimization-based adsorption study.
Sign Up to like & getrecommendations! Published in 2019 at "Journal of colloid and interface science"
DOI: 10.1016/j.jcis.2019.05.007
Abstract: In this research paper, response surface methodology (RSM), generalized regression neural network (GRNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to develop prediction models for Triclosan (TCS) removal by a novel inclusion complex (host-guest… read more here.
Keywords: grnn; error; removal; host guest ... See more keywords
Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique.
Sign Up to like & getrecommendations! Published in 2018 at "Journal of environmental management"
DOI: 10.1016/j.jenvman.2018.06.033
Abstract: In the present study three different types of neural models: multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis function (RBF) has been used to predict the exergetic efficiency of roughened solar air… read more here.
Keywords: solar air; grnn; air; model ... See more keywords
State prediction of MR system by VMD-GRNN based on fractal dimension
Sign Up to like & getrecommendations! Published in 2022 at "Advances in Mechanical Engineering"
DOI: 10.1177/16878132221145899
Abstract: Taking the test signals of magneto-rheological vibration system under different states as research objects, four Generalized Regression Neural Network (GRNN) prediction algorithms, based on time series, time series Auto-Regressive (AR) model coefficients, time series box… read more here.
Keywords: system; prediction algorithms; prediction; dimension ... See more keywords
GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification
Sign Up to like & getrecommendations! Published in 2023 at "Algorithms"
DOI: 10.3390/a16030126
Abstract: In recent years, graph neural networks (GNNs) have played an important role in graph representation learning and have successfully achieved excellent results in semi-supervised classification. However, these GNNs often neglect the global smoothing of the… read more here.
Keywords: network; classification; semi supervised; graph ... See more keywords