Trend estimation and univariate forecast of the sunspot numbers: Development and comparison of ARMA, ARIMA and Autoregressive Neural Network models

2011 ◽  
Vol 343 (7) ◽  
pp. 433-442 ◽  
Author(s):  
Surajit Chattopadhyay ◽  
Deepak Jhajharia ◽  
Goutami Chattopadhyay
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Trend Estimation ◽  
Sunspot Numbers ◽  
Autoregressive Neural Network

scholarly journals Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1691 ◽  
Author(s):  
Fabio Pereira ◽  
Francisco Bezerra ◽  
Shigueru Junior ◽  
Josemir Santos ◽  
Ivan Chabu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Neural Network Models ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

scholarly journals Modeling of Nonlinear Autoregressive Neural Network for Multi-Step Ahead Air Quality Prediction

TEM Journal ◽  
2020 ◽  
pp. 852-861
Author(s):  
Mirza Pasic ◽  
Izet Bijelonja ◽  
Edin Kadric ◽  
Hadis Bajric
Keyword(s):  
Neural Network ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Network Models ◽  
Quality Prediction ◽  
Optimal Model ◽  
Neural Network Models ◽  
Air Quality Prediction ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

In this paper five neural network models were developed using NARX-SP neural network type in order to predict air pollutants concentrations (SO2, PM10, NO2, O3 and CO ) for the 72nd hour ahead for Sarajevo. Hourly values of air pollutants concentrations and meteorological parameters (air temperature, pressure and humidity, wind speed and direction) for Sarajevo were used. Optimal model was selected based on the values of R2, MSE and the complexity of models. Optimal neural network model can predict air pollutants concentrations for the 72nd hour ahead with high accuracy, as well as for all hours up to 72nd hour.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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