Nonlinear auto-regressive neural network model for forecasting Hi-Def H.265 video traffic over Ethernet Passive Optical Networks

2017 ◽  
Author(s):  
Collin Daly ◽  
David L. Moore ◽  
Rami J. Haddad
Keyword(s):  
Neural Network ◽  
Optical Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Passive Optical Networks ◽  
Video Traffic ◽  
Auto Regressive ◽  
Ethernet Passive Optical Networks

Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data

2018 ◽  
Vol 233 (7) ◽  
pp. 2549-2569
Author(s):  
Farrukh Mazhar ◽  
Mohammad A Choudhry ◽  
Muhammad Shehryar
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flight Test ◽  
Aerial Vehicle ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Trained Neural Network

Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.

Hybrid auto-regressive neural network model for estimating global solar radiation in Bandar Abbas, Iran

2016 ◽  
Vol 75 (2) ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Kasra Mohammadi ◽  
Jamshid Piri ◽  
Dalibor Petković ◽  
Ahmad Karim
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Network Model ◽  
Neural Network Model ◽  
Global Solar Radiation ◽  
Auto Regressive ◽  
Bandar Abbas

Optimized Intelligent Auto-Regressive Neural Network Model (ARNN) for Prediction of Non-Linear Exogenous Signals

2021 ◽  
Author(s):  
Lubna Farhi ◽  
Agha Yasir
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Computational Cost ◽  
Error Component ◽  
Design Parameters ◽  
Non Linear ◽  
Auto Regressive ◽  
Comparison Of The Results ◽  
Intelligent Approach

Abstract The paper presents a prediction of non-linear exogenous signal by optimized intelligent auto-regressive neural network model (ARNN). A signal comprises of two sets of data called deterministic and error. The former type of data represents the degradation index of a signal, while the error is the uncertainties associated with the signal. To understand and predict signals, a intelligent approach is taken through the use of ARNN model. In this approach, the rst step is to diagnose whether a time series signal is normally distributed or not by utilizing the Jarque-Bera test. The high and low volatility data ele- ments can be separated via kurtosis hypothesis. The deterministic component of the signal is also predicted by developing a neural network based non-linear autoregressive model (NN-NARX) and the error component by using a linear model. The nal forecast is formed by combining the results determined from each of the models and evaluated using the mean square error results. Vali- dation of the prediction is obtained through a comparison of the results with other models such as ARNN, traditional ARMX, and NARX models. The re- sults show that the proposed model provides improved predictions, minimize high dependence on design parameters with low computational cost.

scholarly journals Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries

2020 ◽  
Vol 11 (3) ◽  
pp. 49-52
Author(s):  
Tayyab Raza Fraz ◽  
Samreen Fatima
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Economic Variables ◽  
Forecast Performance ◽  
Non Linear ◽  
Auto Regressive

Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statisticaland econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presenceof structural break, linear models are failed to model and forecast. Therefore, this study examines the forecastingperformance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, United Kingdom andUnited States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR)and Auto regressive integrated moving average model (ARIMA) models. The economic variables are inflation,exchange rate and Gross Domestic Product (GDP) growth for the period from 1970 to 2015. To measure theperformance of the considered model Root, Mean Square Error, Mean Absolute Error and Mean Absolute PercentageError are used. The results show that the forecasts from the non-linear neural network model are undoubtedly better ascompared to the AR and the Box–Jenkins ARIMA models.

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