scholarly journals The Efficiency of Artificial Neural Networks for Forecasting in the Presence of Autocorrelated Disturbances

2016 ◽  
Vol 5 (2) ◽  
pp. 51 ◽  
Author(s):  
Alexander K White ◽  
Samir K Safi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Regression Models ◽  
Nonlinear Models ◽  
Moving Average ◽  
Real Data ◽  
Model Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Artificial Neural ◽  
Autocorrelated Errors

<p>We compare three forecasting methods, Artificial Neural Networks (ANNs), Autoregressive Integrated Moving Average (ARIMA) and Regression models. Using computer simulations, the major finding reveals that in the presence of autocorrelated errors ANNs perform favorably compared to ARIMA and regression for nonlinear models. The model accuracy for ANN is evaluated by comparing the simulated forecast results with the real data for unemployment in Palestine which were found to be in excellent agreement.</p>

PERBANDINGAN ARIMA DAN ARTIFICIAL NEURAL NETWORKS DALAM PERAMALAN JUMLAH POSITIF COVID-19 DI DKI JAKARTA

2021 ◽  
Vol 10 (3) ◽  
pp. 288-301
Author(s):  
Tri Wahyuni ◽  
Indahwati Indahwati ◽  
Kusman Sadik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Root Mean Square Error ◽  
Moving Average ◽  
Percentage Error ◽  
Cumulative Number ◽  
Mean Square ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Artificial Neural

DKI Jakarta is the center of the spread of Covid-19. This is indicated by the higher cumulative number of Covid-19 positive in DKI Jakarta compared to other provinces. The high number of cases in DKI Jakarta is a concern for all groups, so it is necessary to do forecasting to predict the number of Covid-19 positive in the next period. Accurate forecasting is needed to get better results. This study compares the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in predicting the number of Covid-19 positive in DKI Jakarta. Forecasting accuracy is calculated using the value of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The results show that the best model for forecasting the number of Covid-19 positive in DKI Jakarta is ARIMA(0,1,1) with drift, with a MAPE value of 15.748, an RMSE of 268.808, and the correlation between the forecast value and the actual value of 0.845. Forecasting using ARIMA(0,1,1) with drift and BP(3,10,1) models produces the best forecast for the long forecasting period of the next six weeks.

scholarly journals Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Adnan Rachmat Anom Besari ◽  
Ruzaidi Zamri ◽  
Md. Dan Md. Palil ◽  
Anton Satria Prabuwono
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Surface Defects ◽  
Vision System ◽  
Real Data ◽  
Surface Measurement ◽  
Vision Sensor ◽  
Skilled Workers ◽  
Artificial Neural

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks

scholarly journals ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

2018 ◽  
Vol 36 (4) ◽  
pp. 891
Author(s):  
Ouorou Ganni Mariel GUERA ◽  
José Antônio Aleixo SILVA ◽  
Rinaldo Luiz Caraciolo FERREIRA ◽  
Héctor Barrero MEDEL ◽  
Daniel Álvarez LAZO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Regression Models ◽  
Growth Models ◽  
Pinus Caribaea ◽  
Three Stages ◽  
Artificial Neural ◽  
Sigmoidal Growth ◽  
Pinus Caribaea Morelet

The present study was carried out to compare the performances of regression models and Artificial Neural  Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type  and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which  Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three  stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

2021 ◽  
Author(s):  
özlem karadag albayrak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Moving Average ◽  
Renewable Energy Sources ◽  
Energy Generation ◽  
Energy Sources ◽  
Ann Model ◽  
Artificial Neural ◽  
Renewable Energy Generation

Abstract Turkey attaches particular importance to energy generation by renewable energy sources in order to remove negative economic, environmental and social effects caused by fossil resources in energy generation. Renewable energy sources are domestic and do not have any negative effect, such as external dependence in energy and greenhouse gas, caused by fossil resources and which constitute a threat for sustainable economic development. In this respect, the prediction of energy amount to be generated by Renewable Energy (RES) is highly important for Turkey. In this study, a generation forecasting was carried out by Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods by utilising the renewable energy generation data between 1965-2019. While it was predicted by ANN that 127.516 TWh energy would be generated in 2023, this amount was estimated to be 45.457 TeraWatt Hour (TWh) by ARIMA (1.1.6) model. The Mean Absolute Percentage Error (MAPE) was calculated in order to specify the error margin of the forecasting models. This value was determined to be 13.1% by ANN model and 21.9% by ARIMA model. These results suggested that the ANN model provided a more accurate result. It is considered that the conclusions achieved in this study will be useful in energy planning and management.

scholarly journals Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1923
Author(s):  
Eduardo G. Pardo ◽  
Jaime Blanco-Linares ◽  
David Velázquez ◽  
Francisco Serradilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Reforming ◽  
Real Data ◽  
Memetic Algorithms ◽  
Black Box ◽  
Optimization Techniques ◽  
Ann Model ◽  
The Real ◽  
Artificial Neural

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Air Quality in Urban Areas: Case of Tunisia

2012 ◽  
Vol 518-523 ◽  
pp. 2969-2979 ◽  
Author(s):  
Ayari Samia ◽  
Nouira Kaouther ◽  
Trabelsi Abdelwahed
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Urban Areas ◽  
Moving Average ◽  
Warning System ◽  
Model Combining ◽  
Ann Models ◽  
Artificial Neural

Forecasting air quality time series represents a very difficult task since air quality contains autoregressive, linear and nonlinear patterns. Autoregressive Integrated Moving Average (ARIMA) models have been widely used in air quality time series forecasting. However, they fail to detect extreme events because of their presumed linear form of data. Artificial Neural Networks (ANN) models have proved to be promising nonlinear tools for air quality forecasting. A hybrid model combining ARIMA and ANN improved forecasting more than either of the models used independently. Experimental results with meteorological and Particulate Matter data indicated that the combined model can be used as an efficient forecasting and early warning system for providing air quality information towards the citizen, not only in Sfax Southern Suburbs but in other Tunisian regions that suffer from poor air quality conditions.

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