scholarly journals A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 620 ◽  
Author(s):  
Zina Boussaada ◽  
Octavian Curea ◽  
Ahmed Remaci ◽  
Haritza Camblong ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Network Model ◽  
Neural Network Model ◽  
Direct Solar Radiation ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

scholarly journals Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019 ◽  
Vol 11 (23) ◽  
pp. 6535 ◽  
Author(s):  
Kim ◽  
Seong ◽  
Choi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Missing Values ◽  
Training Data ◽  
Model Parameters ◽  
Cooling Load ◽  
Cooling Loads ◽  
Nonlinear Autoregressive ◽  
Narx Neural Network

Accurate calculations and predictions of heating and cooling loads in buildings play an important role in the development and implementation of building energy management plans. This study aims to improve the forecasting accuracy of cooling load predictions using an optimized nonlinear autoregressive exogenous (NARX) neural network model. The preprocessing of training data and optimization of parameters were investigated for model optimization. In predictive models of cooling loads, the removal of missing values and the adjustment of structural parameters have been shown to help improve the predictive performance of a neural network model. In this study, preprocessing the training data eliminated missing values for times when the heating, ventilation, and air-conditioning system is not running. Also, the structural and learning parameters were adjusted to optimize the model parameters.

A NARX Neural Network Model To Predict One-Day Ahead Movement of The Stock Market Index of Malaysia

2021 ◽  
Author(s):  
Ali H. Dhafer ◽  
Fauzias Mat Nor ◽  
Wahidah Hashim ◽  
Nuradli Ridzwan Shah ◽  
Khairil Faizal Bin Khairi ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Model ◽  
Neural Network Model ◽  
Stock Market Index ◽  
Market Index ◽  
Narx Neural Network

scholarly journals Analysis of Bus Trip Characteristics and Demand Forecasting Based on NARX Neural Network Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Feng Sun ◽  
Wenheng Su ◽  
Weixuan Liu ◽  
Hui Cao ◽  
Dong Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Arima Model ◽  
Demand Forecasting ◽  
The Elderly ◽  
Ic Card ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Narx Neural Network

In recent years, there has been increased interest in the use of bus IC card data to analyze bus transit time characteristics, and the prediction is no longer confined to rail traffic passenger flow prediction and traditional traffic flow prediction. Research on passenger flow forecast for the bus IC card has been increasing year by year. Based on the bus IC card data of Qingdao City, this paper first analyzes the characteristics of one-day passenger flow and passenger flow during subperiods and conducts a separate study on the characteristics of the elderly. The results show that the travel of the elderly is also affected by the weekday and the weekend. Then, based on the ARIMA model and the NARX neural network model, the passenger flow forecasting (10-minute interval) is carried out using the IC card data of No. 1 bus for 5 weekdays. The prediction results show that the NARX neural network model is effective in the short-term prediction of bus passenger flow, and especially, it is more accurate in the peak hour and large-scale data prediction.

scholarly journals Adaptive Control of Meniscus Velocity in Continuous Caster based on NARX Neural Network Model

2019 ◽  
Vol 52 (29) ◽  
pp. 222-227 ◽  
Author(s):  
Shereen Abouelazayem ◽  
Ivan Glavinić ◽  
Thomas Wondrak ◽  
Jaroslav Hlava
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Network Model ◽  
Neural Network Model ◽  
Continuous Caster ◽  
Narx Neural Network

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

2019 ◽  
Vol 22 (12) ◽  
pp. 2712-2723 ◽  
Author(s):  
Xu Han ◽  
Huoyue Xiang ◽  
Yongle Li ◽  
Yichao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dynamic Systems ◽  
Artificial Neural Network Model ◽  
Stochastic Excitations ◽  
Training Samples ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

To improve the efficiency of reliability calculations for vehicle-bridge systems, we present a surrogate modeling method based on a nonlinear autoregressive with exogenous input artificial neural network model and an important sample, which can forecast responses of dynamic systems, such as vehicle-bridge systems, subjected to stochastic excitations. We also propose a process to analyze the method. A quarter-vehicle model is used to verify the proposed method’s precision, and the nonlinear autoregressive with exogenous input artificial neural network model is used to predict responses of vertical vehicle-bridge systems. The results show that, compared to other training samples, the nonlinear autoregressive with exogenous input artificial neural network model has better prediction accuracy when the sample with the maximum response is considered as an important sample and is used to train the nonlinear autoregressive with exogenous input artificial neural network model, and it requires only two-time numerical simulation (or Monte Carlo simulation) at most, which is used in the training of the nonlinear autoregressive with exogenous input artificial neural network model.

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