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Resilient Neural Forecasting Systems [article]

Michael Bohlke-Schneider, Shubham Kapoor, Tim Januschowski
2022 arXiv   pre-print
In this paper, we discuss data challenges and solutions in the context of a Neural Forecasting application on labor planning.We discuss how to make this forecasting system resilient to these data challenges  ...  This results in a fully autonomous forecasting system that compares favorably to a hybrid system consisting of the algorithm and human overrides.  ...  In our system, the implementation of these approaches leads to Resilient Neural Forecasting Systems. We find that our system performs on par or better than human assisted forecasts.  ... 
arXiv:2203.08492v1 fatcat:hd53gwmvhbevhbxoxtrty2q4ji

Smart IOT based Short Term Forecasting of Power Generation Systems and Quality Improvement Using Resilient Back Propagation Neural Network

Rafeek Ahmed S
2021 Revista GEINTEC  
But the proposed Resilient Back Propagation Neural Network (RBPN) is suitable for STF modeling and also the proposed forecasting system is directly connected to IEEE-9 bus to reduce Total Harmonics Distortion  ...  Therefore, this work presents a Resilient Back Propagation Neural Network (RBPN) model to produce solar and wind power Short Term Forecasting (STF) and monitoring using Internet of Things (IoT).  ...  The article concentrates on the smart IoT based short term forecasting of power generation systems and quality improvement using resilient back propagation neural network model.  ... 
doi:10.47059/revistageintec.v11i3.2004 fatcat:itrm2h3bxjfovkpxihyy6v37mu

A variational mode decompoisition approach for analysis and forecasting of economic and financial time series

Salim Lahmiri
2016 Expert systems with applications  
The resilient back-propagation neural network Artificial neural networks (ANN) are computational intelligent systems developed to mathematically mimic the computational operations of the human brain.  ...  The first 1184 observations are used for training the resilient back-propagation neural network, whilst the last 132 observations are used for out-of-sample forecasting.  ... 
doi:10.1016/j.eswa.2016.02.025 fatcat:dcfp2zvffzedvagzmjbfydsnyy

Cyber-Physical Microgrids: Toward Future Resilient Communities [article]

Tuyen Vu, Bang Nguyen, Zheyuan Cheng, Mo-Yuen Chow, Bin Zhang
2019 arXiv   pre-print
resilient control systems.  ...  As cyber-physical systems, microgrids are not immune to these threats.  ...  - 3 System FIGURE 4 - 4 Forecasting FIGURE 5 - 5 Key resilience control objectives.  ... 
arXiv:1912.05682v1 fatcat:nqmvi2rpmreate75cbrhm7bdtu

A neuro-forecastwater-filling scheme of server scheduling

Homayoun Yousefi'zadeh
2006 2006 International Conference on Systems and Networks Communications (ICSNC'06)  
The schemes utilize BFGS and resilient backpropagation learning in perceptron neural networks to forecast the arriving traffic patterns, respectively.  ...  Dynamic server scheduling schemes in queuing systems accommodating delay-sensitive traffic need to address the tradeoff between efficiency and fairness.  ...  We utilized BFGS and resilient back-propagation learning schemes in a fixed structure neural network to forecast traffic patterns.  ... 
doi:10.1109/icsnc.2006.6 dblp:conf/icsnc/Yousefizadeh06 fatcat:zhaqyjwni5hwpaxq6lsnj26n5y

Epoch Analysis and Accuracy 3 ANN Algorithm Using Consumer Price Index Data in Indonesia

M Fauzan, Anjar Wanto, Dedi Suhendro, Iin Parlina, Bahrudi Efendi Damanik, Pani Akhiruddin Siregar, Nani Hidayati
2018 Zenodo  
This research uses Backpropagation Algorithm, Conjugate Gradient Fletcher-Reeves (CGFR) and Resilient.  ...  So it can be concluded that the use of backpropagation algorithm and gradient fletcher reeves to produce iteration and accuracy level better when compared with Resilient Algorithm  ...  There are several Artificial Neural Network Algorithms that are often used for forecasting, among others: Backpropagation Algorithm, Conjugate Gradient Fletcher-Reeves (CGFR) And Resilient.  ... 
doi:10.5281/zenodo.2532434 fatcat:3r7ktm26wjchpiaonqtu7c4luy

Wind Power Prediction Using Neural Networks with Different Training Models

Sana Mohsin Babbar, Tameer Hussain Langah
2022 Indonesian Journal of Innovation and Applied Sciences (IJIAS)  
Consequently, machine learning tools specifically neural networks have created a huge impact in forecasting wind power.  ...  Therefore, forecasting and prediction are promising solutions to address mismanagement at the grid.  ...  For having an efficient energy management system, forecasting plays an important role.  ... 
doi:10.47540/ijias.v2i1.340 fatcat:7k4zqsesvjbt3pssnlh3mqdja4

A Comparative Study Of Backpropagation Algorithms In Financial Prediction

Salim Lahmiri
2011 International Journal of Computer Science Engineering and Applications  
The accuracy of backpropagation neural networks trained with different heuristic and numerical algorithms is measured for comparison purpose.  ...  Artificial neural networks have been widely employed to predict financial stock market levels thanks to their ability to model nonlinear functions.  ...  The artificial neural networks are adaptive nonlinear systems capable to approximate any function.  ... 
doi:10.5121/ijcsea.2011.1402 fatcat:x7igqvnmffb3xmsqdwmno4daum

Optimizing Feed Forward Backpropagation Neural Network Based on Teaching-Learning-Based Optimization Algorithm for Long-Term Electricity Forecasting

2022 International Journal of Intelligent Engineering and Systems  
Electricity load forecasting has an important role in the energy management system.  ...  Power system management can be said to be good if the planning carried out will make a major contribution to the development of electric power systems.  ...  Literature review 2.1 A feed forward backpropagation neural network (FFBNN) Artificial neural network is a computing system that duplicates the skill of the brain.  ... 
doi:10.22266/ijies2022.0228.02 fatcat:gigdt5z5mfhi7joeg7rhs5dfpq

A robust recurrent ANFIS for modeling multi-step-ahead flood forecast of Three Gorges Reservoir in the Yangtze River

Yanlai Zhou, Fi-John Chang, Shenglian Guo, Huanhuan Ba, Shaokun He
2017 Hydrology and Earth System Sciences Discussions  
Accurate and robust multi-step-ahead flood forecast during flood season is extremely crucial to reservoir flood control.  ...  Classic, Recurrent, and Modified Recurrent) models with their optimal input variables identified by the Gamma Test are utilized for modeling multi-step-ahead flood forecast of Three Gorges Reservoir in  ...  Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia. Hydrology and Earth System Sciences, 16(4), 1151-1169. Firat, M., Güngör, M., 2008.  ... 
doi:10.5194/hess-2017-457 fatcat:6rl5bk2mpbag7dojl2nwvs6tzq

A Hybrid Model of Autoregressive Integrated Moving Average and Artificial Neural Network for Load Forecasting

Lemuel Clark P Velasco, Daisy Lou, Gary Paolo, Michael Bryan, Felicisimo B.
2019 International Journal of Advanced Computer Science and Applications  
In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as a hybrid forecasting model for a power utility's dataset in order to predict the  ...  error for electric load forecasting.  ...  systems.  ... 
doi:10.14569/ijacsa.2019.0101103 fatcat:tcflkaxranb4nfmfnksumja7ji

Prediction of Daily Global Solar Radiation using Resilient-propagation Artificial Neural Network and Historical Data: A Case Study of Hail, Saudi Arabia

S. Boubaker, S. Kamel, M. Kchaou
2020 Zenodo  
In this paper, several different Feed Forward Artificial Neural Networks (FFANNs) were used for forecasting the one-day-ahead Global Horizontal Irradiation (GHI) in Hail region, Saudi Arabia.  ...  The main motivation behind predicting GHI is that it is a critical parameter in sizing and planning photovoltaic water pumping systems.  ...  Based on multiple neural networks and using a photovoltaic panel (PV) model associated with an irradiance forecast, a PV yield prediction system has been presented in [2] .  ... 
doi:10.5281/zenodo.3659562 fatcat:2ub42ujsqbabjelsnj7bmqwqja

An Efficient Patient Inflow Prediction Model For hospital Resource Management

Kottalanka Srikanth, D. Arivazhagan
2017 Indonesian Journal of Electrical Engineering and Computer Science  
To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network.  ...  The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network.  ...  To address this here we use resilient back propagation neural network for training PIP data. The resilient algorithms are used to remove the impact of magnitude of the derivative functions.  ... 
doi:10.11591/ijeecs.v7.i3.pp809-817 fatcat:i4ejabowsjftrmvspefbgrffi4


Kottalanka Srikanth, D Arivazhagan
2017 ICTACT Journal on Soft Computing  
To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network.  ...  The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network.  ...  To address this, resilient back propagation neural network for training PIP data is used. The resilient algorithms are used to remove the impact of magnitude of the derivative functions.  ... 
doi:10.21917/ijsc.2017.0208 fatcat:4kvshyalt5agnnamhzo3w6qgty

Design of a High Sea Wave Sensor System in Puger Beach

Ike Fibriani, Januar Fery Irawan, Alfredo Bayu Satriya, Satrio Budi Utomo, Widyono Hadi, Widjonarko Widjonarko, Khoiril Khoiril
2020 JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering)  
The wave height in the sea was predicted by calculating the wind speed value and effective average fetch value using neural network algorithm.  ...  The wave height measurement system worked out and found the average wave height in Puger Beach 0.37 meters.  ...  To forecast wave height using windspeed through neural network, the actual data gathering was conducted.  ... 
doi:10.22219/jemmme.v5i2.12552 fatcat:lkm6iet6xvclhiruyo2ajtkxja
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