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.

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

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

A Computationally Efficient Methodology for Generating Training Data for a Transient Neural Network of a Tip-Jet Reaction Drive System

2011 ◽  
Vol 133 (12) ◽  
Author(s):  
Brian K. Kestner ◽  
Jimmy C.M. Tai ◽  
Dimitri N. Mavris
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Network Model ◽  
Neural Network Model ◽  
Operating Conditions ◽  
Drive System ◽  
Training Data ◽  
Computationally Efficient ◽  
Model Based Control ◽  
Training Methodology

This paper presents a computationally efficient methodology for generating training data for a transient neural network model of a tip-jet reaction drive system for potential use as an onboard model in a model based control application. This methodology significantly reduces the number of training points required to capture the transient performance of the system. The challenge in developing an onboard model for a tip-jet reaction drive system is that the model has to operate over the whole flight envelope, to account for the different dynamics present in the system, and to adjust to system degradation or potential faults. In addition, the onboard model must execute in less time than the update interval of the controller. To address these issues, a computationally efficient training methodology and neural network surrogate model have been developed that captures the transient performance of the tip-jet reaction system. As the number of inputs to a neural network becomes large, the computational time needed to generate the number of training points required to accurately represent the range of operating conditions of the system may become quite large also. A challenge for the tip-jet reaction drive system is to minimize the number of neural network training points, while maintaining the high accuracy. To address this issue, a novel training methodology is presented which first trains a steady-state neural network model and uses deviations from steady-state operating conditions to define the transient portion of the training data. The combined results from both the transient and the steady-state training data can then be used to create a single transient neural network of the system. The results in this paper demonstrate that a transient neural network using this new computationally efficient training methodology has the potential to be a feasible option for use as an onboard real-time model for model based control of a tip-jet reaction drive system.

scholarly journals A Graph Neural Network for Predicting Energy and Stability of Known and Hypothetical Crystal Structures

2021 ◽  
Author(s):  
Shubham Pandey ◽  
Jiaxing Qu ◽  
Vladan Stevanovic ◽  
Peter St. John ◽  
Prashun Gorai
Keyword(s):  
Neural Network ◽  
Total Energy ◽  
Network Model ◽  
Neural Network Model ◽  
Density Functional ◽  
Failure Modes ◽  
Inorganic Materials ◽  
Training Data ◽  
The Neural Network ◽  
Unique Graph

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerating the screening for new materials over vast chemical spaces. Here, we develop a unique graph neural network model to accurately predict the total energy of both GS and higher-energy hypothetical structures. We use ~16,500 density functional theory calculated total energy from the NREL Materials Database and ~11,000 in-house generated hypothetical structures to train our model, thus making sure that the model is not biased towards either GS or higher-energy structures. We also demonstrate that our model satisfactorily ranks the structures in the correct order of their energies for a given composition. Furthermore, we present a thorough error analysis to explain several failure modes of the model, which highlights both prediction outliers and occasional inconsistencies in the training data. By peeling back layers of the neural network model, we are able to derive chemical trends by analyzing how the model represents learned structures and properties.

scholarly journals A Comparison of Regularization Techniques in Deep Neural Networks

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 648 ◽  
Author(s):  
Ismoilov Nusrat ◽  
Sung-Bong Jang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Training Data ◽  
Regularization Techniques ◽  
Performance Results ◽  
Normalization Scheme ◽  
Significant Attention

Artificial neural networks (ANN) have attracted significant attention from researchers because many complex problems can be solved by training them. If enough data are provided during the training process, ANNs are capable of achieving good performance results. However, if training data are not enough, the predefined neural network model suffers from overfitting and underfitting problems. To solve these problems, several regularization techniques have been devised and widely applied to applications and data analysis. However, it is difficult for developers to choose the most suitable scheme for a developing application because there is no information regarding the performance of each scheme. This paper describes comparative research on regularization techniques by evaluating the training and validation errors in a deep neural network model, using a weather dataset. For comparisons, each algorithm was implemented using a recent neural network library of TensorFlow. The experiment results showed that an autoencoder had the worst performance among schemes. When the prediction accuracy was compared, data augmentation and the batch normalization scheme showed better performance than the others.

Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2014 ◽  
Vol 574 ◽  
pp. 342-346
Author(s):  
Hong Yan Duan ◽  
Huan Rong Zhang ◽  
Ming Zheng ◽  
Xiao Hong Wang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Approximate Methods ◽  
The Neural Network

The fracture problems of medium carbon steel under extra-low cycle bend torsion fatigue loading were studied using artificial neural networks (ANN) in this paper. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and notch open angle, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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
Sign in / Sign up

Export Citation Format

Share Document