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Training of Artificial Neural Networks Using Information-Rich Data

Shailesh Singh, Sharad Jain, András Bárdossy
2014 Hydrology  
Artificial Neural Networks (ANNs) are classified as a data-driven technique, which implies that their learning improves as more and more training data are presented.  ...  In this study, the data depth function was used as a tool for the identification of critical (information) segments in a time series, which does not depend on large variation in magnitude, scale or the  ...  Acknowledgments The work described in this paper was supported by a scholarship program initiated by the German Federal Ministry of Education and Research (BMBF) under the program of the International  ... 
doi:10.3390/hydrology1010040 fatcat:yfgxos5dgjgo7g2vzcw4jvbvti

Obtaining Modal Parameters in Steel Model Bridge by System Identification using Artificial Neural Networks

Hakan Aydin
2020 Zenodo  
Artificial Neural Networks are easy to build and take good care of large amounts of noisy data. They are especially suitable for the solution of nonlinear problems.  ...  Hakan Aydin "Obtaining Modal Parameters in Steel Model Bridge by System Identification using Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development  ...  The results show that the artificial neural network is more suitable than other traditional methods to estimate the phase discharge relationship.  ... 
doi:10.5281/zenodo.3854858 fatcat:lfgtxway6zb6phmgw2dv7jc5f4

Early stage white etching crack identification using artificial neural networks

Xiaodi Liu, Baher Azzam, Freia Harzendorf, Johann Kolb, Ralf Schelenz, Kay Hameyer, Georg Jacobs
2021 Forschung im Ingenieurwesen  
AbstractWhite Etching Cracks (WEC) in gearbox bearings is a major concern in the wind turbine industry, which can lead to a premature failure of the gearbox.  ...  A Long Short Term Memory (LSTM) network-based autoencoder is proposed for the anomaly detection (AD) task.  ...  Thus, a gap exists between the state of the art and this research's goal.  ... 
doi:10.1007/s10010-021-00481-y fatcat:voyul3wzbfeoholerd4p3bhvxm

Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy

M.T. Makhloufi, M.S. Khireddine, Y. Abdessemed, A. Boutarfa
2014 International Journal of Intelligent Systems and Applications  
In this paper, a simulation study of the maximum power point tracking (M PPT) for a photovoltaic system using an artificial neural network is presented.  ...  Finally performance comparison between artificial neural network controller and Perturb and Observe method has been carried out which has shown the effectiveness of artificial neural networks controller  ...  Artificial Neural Network controller method The MPPT strategy proposed here consists of a combination of an artificial neural network and the MPPT technique in order to imp lement of the duty cycle regulator  ... 
doi:10.5815/ijisa.2014.12.03 fatcat:tjw7szih2zeynpruz4eprttaju

A review of evidence of health benefit from artificial neural networks in medical intervention

P.J.G. Lisboa
2002 Neural Networks  
The rôle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence.  ...  This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical  ...  Some of these involve artificial neural networks.  ... 
doi:10.1016/s0893-6080(01)00111-3 pmid:11958484 fatcat:oj73bxwth5ft3gd7y2wegr7lbe

Implementation of artificial neural network technique in the simulation of dam breach hydrograph

Vahid Nourani, Habib Hakimzadeh, Alireza Babaeyan Amini
2012 Journal of Hydroinformatics  
In the present study, two artificial neural networks were developed to simulate outflow hydrograph from earthen dam breach.  ...  The obtained results demonstrate that the results of the artificial neural network (ANN) method are in good agreement with the observed values, and this method produces better results than existing classical  ...  The artificial neural network (ANN), as such a black-box approach, is capable of identifying complex nonlinear relationships between input and output data sets and is used widely to model countless  ... 
doi:10.2166/hydro.2011.114 fatcat:ryzfk7znabdjzai3pdg3l7lmpy

Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling [article]

Nathan A. Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash
2022 arXiv   pre-print
A way to bypass this bottleneck is to deploy artificial neural network surrogates for rapid evaluations of the necessary plasma quantities.  ...  However, one issue with training an accurate artificial neural network surrogate is the reliance on a sufficiently large and representative data set for both training and validation, which can be time-consuming  ...  One technique to obtain a high-fidelity function approximation is through the use of a multilayered perceptron (MLP) architecture, which is a subclass of feedforward artificial neural network.  ... 
arXiv:2112.05325v2 fatcat:amjfdzthcfax5g6tbvmec7tqdq

Artificial neural networks for machining processes surface roughness modeling

Fabricio J. Pontes, João R. Ferreira, Messias B. Silva, Anderson P. Paiva, Pedro Paulo Balestrassi
2009 The International Journal of Advanced Manufacturing Technology  
In recent years, several papers on machining processes have focused on the use of artificial neural networks for modeling surface roughness.  ...  Even in such a specific niche of engineering literature, the papers differ considerably in terms of how they define network architectures and validate results, as well as in their training algorithms,  ...  Acknowledgments The authors would like to express their gratitude to FAPEMIG, CAPES, and CNPq for their support in this research.  ... 
doi:10.1007/s00170-009-2456-2 fatcat:jo5sodjopvcgvo2ep6shyvulbu

An artificial neural network model for generating hydrograph from hydro-meteorological parameters

Sajjad Ahmad, Slobodan P. Simonovic
2005 Journal of Hydrology  
A feed-forward artificial neural network is trained by using back-percolation algorithm.  ...  Artificial neural networks (ANN) can be an efficient way of modeling the runoff process in situations where explicit knowledge of the internal hydrologic processes is not available.  ...  Acknowledgements The authors would like to thank Mr Alf Warkentin from the Water Resource Branch, Manitoba Department of Conservation, for providing the necessary data.  ... 
doi:10.1016/j.jhydrol.2005.03.032 fatcat:mw4vrxgg5jghtfbddbdpgeqljm

Artificial neural networks in hardware: A survey of two decades of progress

Janardan Misra, Indranil Saha
2010 Neurocomputing  
implementation Optical neural network a b s t r a c t This article presents a comprehensive overview of the hardware realizations of artificial neural network (ANN) models, known as hardware neural networks  ...  We outline underlying design approaches for mapping an ANN model onto a compact, reliable, and energy efficient hardware entailing computation and communication and survey a wide range of illustrative  ...  Introduction Hardware devices designed to realize artificial neural network (ANN) architectures and associated learning algorithms especially taking advantage of the inherent parallelism in the neural  ... 
doi:10.1016/j.neucom.2010.03.021 fatcat:regzu6sshvekzd5wxcuaiytgqu

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

Nikolaos M. Vaxevanidis, John D. Kechagias, Nikolaos A. Fountas, Dimitrios E. Manolakos
2015 Open Construction & Building Technology Journal  
The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization  ...  The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding  ...  Nevertheless, accuracy and reliability of results are two main benefits of soft computing techniques such artificial neural networks (ANNs).  ... 
doi:10.2174/1874836801408010389 fatcat:6hx2vysw5zc4fcupsk7kjgs5re

Effluent prediction of chemical oxygen demand from the astewater treatment plant using artificial neural network application

Sani Isa Abba, Gozen Elkiran
2017 Procedia Computer Science  
In this paper, the Artificial neural network (ANNs) was employed to develop and estimate the effluent COD model from the wastewater treatment plant (WWTP), to evaluate the model, the daily recorded data  ...  The ANN performance has been evaluated using statistical techniques (Determination coefficient, RMSE, Correlations), the result of ANNs model was compared with the Multilinear regression analysis (MLR)  ...  Reference (Nourani et al., 2015) used the artificial neural network to modelled the ground water.  ... 
doi:10.1016/j.procs.2017.11.223 fatcat:xkurplag6raldg2umout2lem2u

Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High-Fidelity Hydrodynamics and Water Quality Model

Amelia R. Shaw, Heather Smith Sawyer, Eugene J. LeBoeuf, Mark P. McDonald, Boualem Hadjerioua
2017 Water Resources Research  
The predictive power of the high-fidelity hydrodynamic and water quality model CE-QUAL-W2 is successfully emulated by an artificial neural network, then integrated into a genetic algorithm optimization  ...  Hydropower optimization using artificial neural network surrogate models of a high-fidelity hydrodynamics and water quality Model. Water Resources Research, 53, 9444-9461. https://doi.  ...  Acknowledgments The authors acknowledge the following people and programs for their support for this project: Robert Sneed and Jeff Gregory (U.S  ... 
doi:10.1002/2017wr021039 fatcat:nl66oupjwzfgrccsv4tydmgvoa

Bi-directional long short term memory using recurrent neural network for biological entity recognition

Rashmi Siddalingappa, Kanagaraj Sekar
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation.  ...  The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network.  ...  A comparison study with the existing systems State-of-art systems R P F Bio-NER using Deep Neural Networks  ... 
doi:10.11591/ijai.v11.i1.pp89-101 fatcat:jst6k5az6bghteqtj66efy6bei

Technological process planning by the use of neural networks

Izabela Rojek
2016 Artificial intelligence for engineering design, analysis and manufacturing  
The use of neural networks makes the creation of such a system possible.  ...  A computer-aided process planning system based on rules and neural network models enables the intelligent design of technological processes.  ...  These articles usually describe the application of computer techniques for solving single tasks, such as the prediction of surface roughness in electrical discharge machining, or the selection of machining  ... 
doi:10.1017/s0890060416000147 fatcat:dt5eit5c3naalf2lcvv3canhoa
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