Artificial NARX Neural Network Model of Wind Speed: Case of Istanbul-Avcilar

2021 ◽  
Author(s):  
Huseyin Calik ◽  
Namik Ak ◽  
Ibrahim Guney
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Network Model ◽  
Neural Network Model ◽  
Narx Neural Network

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

Wind Speed Forecast for Wind Farms Based on Phase Space Reconstruction of Wavelet Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 840-845 ◽  
Author(s):  
Xiao Bing Xu ◽  
Jun He ◽  
Jian Ping Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Space ◽  
Wind Speed ◽  
Network Model ◽  
Neural Network Model ◽  
Phase Space Reconstruction ◽  
Wavelet Neural Network ◽  
Non Linear ◽  
Wind Speed Forecast

Wind speed forecast is a non-linear and non-smooth problem. nonlinear and non-stationary are two kinds of mathematical problem, it is difficult to model with a single method, so that, a wavelet neural network model is set, the non-linear process of wind speed is forecast by neural networks and the non-stationary process of wind speed is decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transforms. wavelet combined with neural network model avoid the neural network model that can not handle non-stationary questions .while, the effect of indefinite inputs are removed by embedding dimension of phase space to determine neural networks inputs. The simulation results show that phase space reconstruction of wavelet neural network is more accuracy than the ordinary BP neural network. It could be well applied in wind speed forecasts.

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 State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 28150-28161 ◽  
Author(s):  
Molla S. Hossain Lipu ◽  
Mahammad A. Hannan ◽  
Aini Hussain ◽  
Mohamad H. M. Saad ◽  
Afida Ayob ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Network Model ◽  
Neural Network Model ◽  
Search Algorithm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Based ◽  
State Of Charge Estimation ◽  
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.

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