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Applications of artificial neural networks in chemical engineering

David M. Himmelblau
<span title="">2000</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2kcxxoesnvhejpjcxydf27w6ja" style="color: black;">Korean Journal of Chemical Engineering</a> </i> &nbsp;
Because ANN are nets of basis functions, they can provide good empirical models of complex nonlinear processes useful for a wide variety of purposes.  ...  A growing literature within the field of chemical engineering describing the use of artificial neural networks (ANN) has evolved for a diverse range of engineering applications such as fault detection,  ...  Thus, feed-forward neural network models have the general structure of y i =f(u) ( 2 ) where f( · ) is a nonlinear mapping.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bf02706848">doi:10.1007/bf02706848</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wl7frq55lngdddj6zrnvmbqlwy">fatcat:wl7frq55lngdddj6zrnvmbqlwy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170813141845/https://www.cheric.org/PDF/KJChE/KC17/KC17-4-0373.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f0/60/f06080d0afdf9f5c0c3c40b96d52ea67f16ee20f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bf02706848"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis

P. Gruber, M. Farhat, P. Odermatt, M. Etterlin, T. Lerch, M. Frei
<span title="2015-12-31">2015</span> <i title="Korean Fluid Machinery Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mbi7agawkncbhlucq77wmegopm" style="color: black;">International Journal of Fluid Machinery and Systems</a> </i> &nbsp;
As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest.  ...  This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and a Francis model  ...  In spite of this, some important conclusions can be drawn from the results of all the experiments: 6.1 Neural net  The neural feed forward net leads to a nonlinear mapping between inputs and target with  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5293/ijfms.2015.8.4.264">doi:10.5293/ijfms.2015.8.4.264</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rcxqjtry7fg4hai3wwmz2v6m5y">fatcat:rcxqjtry7fg4hai3wwmz2v6m5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180725110711/https://infoscience.epfl.ch/record/255344/files/The%20detection%20of%20cavitation%20in%20hydraulic%20machines%20by%20use%20of%20ultrasonic%20signal%20analysis.pdf?version=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/e0/fae00ec0ce257892d7fe0fbcf7688fb7dafde6a6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5293/ijfms.2015.8.4.264"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

The detection of cavitation in hydraulic machines by use of ultrasonic signal analysis

P Gruber, P Odermatt, M Etterlin, T Lerch, M Frei, M Farhat
<span title="2014-03-01">2014</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d5gw7zsbm5fwlb6lvahgsi3ngu" style="color: black;">IOP Conference Series: Earth and Environment</a> </i> &nbsp;
As classifiers two methods were used: neural feed forward networks and decision trees. For both classification methods realizations with lowest complexity as possible are of special interest.  ...  This classification scheme has been applied to different cavitation sections: a sphere in a water flow in circular tube at the HSLU in Lucerne, a NACA profile in a cavitation tunnel and a Francis model  ...  In spite of this, some important conclusions can be drawn from the results of all the experiments: 6.1 Neural net  The neural feed forward net leads to a nonlinear mapping between inputs and target with  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1755-1315/22/5/052005">doi:10.1088/1755-1315/22/5/052005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4lh5tiftrzfuzfif44gfdrfhry">fatcat:4lh5tiftrzfuzfif44gfdrfhry</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180725110711/https://infoscience.epfl.ch/record/255344/files/The%20detection%20of%20cavitation%20in%20hydraulic%20machines%20by%20use%20of%20ultrasonic%20signal%20analysis.pdf?version=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/e0/fae00ec0ce257892d7fe0fbcf7688fb7dafde6a6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1755-1315/22/5/052005"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> iop.org </button> </a>

Spectrum Hole Prediction Based On Historical Data: A Neural Network Approach [article]

Barau Gafai Najashi, Feng Wenjiang, Mohammed Dikko Almustapha
<span title="2014-01-05">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, a neural network based prediction model for predicting the channel status using historical data obtained during a spectrum occupancy measurement is presented.  ...  Genetic algorithm is combined with LM BP for increasing the probability of obtaining the best weights thus optimizing the network.  ...  For this work the feed forward architecture is used.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1401.0886v1">arXiv:1401.0886v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d4w3mrekqbd2di3nlmhlv7dpxu">fatcat:d4w3mrekqbd2di3nlmhlv7dpxu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930115131/https://arxiv.org/ftp/arxiv/papers/1401/1401.0886.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/77/74778da95c2b9345f3e08da5efeb4502ca24996a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1401.0886v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY

Mohamed Genaidy
<span title="2020-09-30">2020</span> <i title="Egypts Presidential Specialized Council for Education and Scientific Research"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7ejh5jsejzbmhe7sraug7m2q6i" style="color: black;">Arab Universities Journal of Agricultural Sciences</a> </i> &nbsp;
The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field  ...  The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural  ...  Sci., 22(3) , 2082 RESULTS AND DISCUSSION Feed Forward Neural Network (FFNN) To make feed forward neural network MLP which had been shown to be applicable at exchange with either linear or nonlinear  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21608/ajs.2020.153538">doi:10.21608/ajs.2020.153538</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gab2mck2ivhwjjz32wkxatk6bi">fatcat:gab2mck2ivhwjjz32wkxatk6bi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210403201337/https://ajs.journals.ekb.eg/article_153538_81be015dcf4b587e472ae4fabcda54af.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/39/6a/396a30a83ca9b03d0100a29ffd6630cd556f1354.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21608/ajs.2020.153538"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

PREDICTING THE RHEOLOGICAL PROPERTIES OF BITUMEN-FILLER MASTIC USING ARTIFICIAL NEURAL NETWORK METHODS

Nursyahirah Khamis, Muhamad Razuhanafi Mat Yazid, Asmah Hamim, Sri Atmaja P. Rosyidi, Nur Izzi Md. Yusoff, Muhamad Nazri Borhan
<span title="2017-12-13">2017</span> <i title="Penerbit UTM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xgym76lve5dw7bbeqkndyfil2u" style="color: black;">Jurnal Teknologi</a> </i> &nbsp;
Two types of ANN models were developed namely; (i) a multilayer feed-forward neural network model and (ii) a radial basis function network model.  ...  A comparison between the two types of models showed that the radial basis function network model has a higher accuracy than multilayer feed-forward neural network model with a higher value of R2 and lower  ...  The authors fully acknowledged Ministry of Higher Education (MOHE) and Universiti Kebangsaan Malaysia for the approved fund which makes this important research viable and effective.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11113/jt.v80.11097">doi:10.11113/jt.v80.11097</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tod7gv3anzclhkac5javhf567e">fatcat:tod7gv3anzclhkac5javhf567e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428120855/https://jurnalteknologi.utm.my/index.php/jurnalteknologi/article/download/11097/6314" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7d/28/7d28627e06bb43d7b0357a297f67bae31f371678.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11113/jt.v80.11097"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Network Learning and Training of a Cascaded Link-Based Feed Forward Neural Network (CLBFFNN) in an Intelligent Trimodal Biometric System

Benson-Emenike Mercy E, Ifeanyi-Reuben Nkechi J
<span title="2018-11-30">2018</span> <i title="Academy and Industry Research Collaboration Center (AIRCC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dlcgzkn4cvbevhkswphjjxglsa" style="color: black;">International Journal of Artificial Intelligence &amp; Applications</a> </i> &nbsp;
In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions  ...  Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own.  ...  ACKNOWLEDGEMENTS The authors would like to appreciate the reviewers of this paper, for their useful comments and contributions which added to the quality of this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/ijaia.2018.9603">doi:10.5121/ijaia.2018.9603</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mo4bsnlnijgtdfzf3wqja5gwk4">fatcat:mo4bsnlnijgtdfzf3wqja5gwk4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190219191611/http://pdfs.semanticscholar.org/1a21/d1889b19e9b85f5ca59259a6873a4aef0af5.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1a/21/1a21d1889b19e9b85f5ca59259a6873a4aef0af5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/ijaia.2018.9603"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Network Learning and Training of a Cascaded Link-Based Feed Forward Neural Network (CLBFFNN) in an Intelligent Trimodal Biometric System

Benson-Emenike Mercy
<span title="2018-12-10">2018</span> <i title="Figshare"> Figshare </i> &nbsp;
In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions  ...  Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own.  ...  ACKNOWLEDGEMENTS The authors would like to appreciate the reviewers of this paper, for their useful comments and contributions which added to the quality of this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.7442018">doi:10.6084/m9.figshare.7442018</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b7thzwt2ijfx3f6qxdzufgm4be">fatcat:b7thzwt2ijfx3f6qxdzufgm4be</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200213072345/https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/13775519/3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bd/90/bd9035f56e8f48af0e066e3cbe9acef2fb4653c4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.7442018"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> figshare.com </button> </a>

A Survey on Rainfall Prediction using Artificial Neural Network

Deepak RanjanNayak, Amitav Mahapatra, Pranati Mishra
<span title="2013-06-26">2013</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear systems.  ...  This paper provides a survey of available literature of some methodologies employed by different researchers to utilize ANN for rainfall prediction.  ...  ACKNOWLEDGEMENTS The authors wish to express sincere thanks to Professor Prashanta Kumar Patra, Head of the Department of Computer Science and Engineering of College of Engineering and Technology for his  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/12580-9217">doi:10.5120/12580-9217</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bknrjutaoze6bjtg4k4emf5qvu">fatcat:bknrjutaoze6bjtg4k4emf5qvu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170813093448/http://research.ijcaonline.org/volume72/number16/pxc3889217.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/08/7f/087f666b1a9cca014b80a7498ccdc8a79bd0a1e2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/12580-9217"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Study of Various Training Algorithms on Neural Network for Angle based Triangular Problem

Amarpal Singh, Piyush Saxena, Sangeeta Lalwani
<span title="2013-06-26">2013</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
This paper examines the study of various feed forward backpropagation neural network training algorithms and performance of different radial basis function neural network for angle based triangular problem  ...  The training algorithms in feed forward back-propagation neural network comprise of Scale Gradient Conjugate Back-Propagation (BP), Conjugate Gradient BP through Polak-Riebre updates, Conjugate Gradient  ...  Feed-forward Back-propagation Neural Networks Backprop implements a gradient descent search through a space of possible network weight, iteratively reducing the error E, between training example and target  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/12420-8988">doi:10.5120/12420-8988</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o72pwsnojnayfgzmtrwcurg3ka">fatcat:o72pwsnojnayfgzmtrwcurg3ka</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706135120/http://research.ijcaonline.org/volume71/number13/pxc3888988.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/72/c372cf0fd8ff7df6fb7b16b7fcff885708c48929.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/12420-8988"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Modified neural network based cascaded control for product composition of reactive distillation

Vandana Sakhre, Sanjeev Jain, V. S. Sapkal, D.P. Agarwal
<span title="2016-06-01">2016</span> <i title="Walter de Gruyter GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nqonoxfsqrfq3mflb7l2rheidm" style="color: black;">Polish Journal of Chemical Technology</a> </i> &nbsp;
In this research work, neural network based single loop and cascaded control strategies, based on Feed Forward Neural Network trained with Back Propagation (FBPNN) algorithm is carried out to control the  ...  Reboiler heat duty is selected as a manipulating variable in case of single loop control strategy while the bottom stage temperature T9 is selected as a manipulating variable for cascaded control strategy  ...  ACKNOWLEDGEMENT The author thankfully acknowledges the fi nancial assistance provided by the All India Council for Technical Education (AICTE), New Delhi (INDIA), under the Research Promotion Scheme (RPS  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1515/pjct-2016-0037">doi:10.1515/pjct-2016-0037</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fxauiqw66jegvm2d23geabjbyu">fatcat:fxauiqw66jegvm2d23geabjbyu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190426141617/https://content.sciendo.com/downloadpdf/journals/pjct/18/2/article-p111.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5b/68/5b6823df4f6b16096b6605ebe5f6c3add05b7821.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1515/pjct-2016-0037"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> degruyter.com </button> </a>

Forecasting Performance of Random Walk with Drift and Feed Forward Neural Network Models

Augustine D. Pwasong, Saratha A\P. Sathasivam
<span title="2015-09-08">2015</span> <i title="MECS Publisher"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/koacvjimprbbvhwasrewxdbiuy" style="color: black;">International Journal of Intelligent Systems and Applications</a> </i> &nbsp;
The linear model considered here is the random walk with drift, while the nonlinear model is the feed forward neural network model.  ...  In this study, linear and nonlinear methods were used to model forecasting performances on the daily crude oil production data of the Nigerian National Petroleum Corporation (NNPC).  ...  The linear method to be involve here is a random walk with drift model, while the nonlinear model to be involve here is a feed forward neural network model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijisa.2015.09.07">doi:10.5815/ijisa.2015.09.07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a5ffuxaqsbfcnjmjulszzrtb7e">fatcat:a5ffuxaqsbfcnjmjulszzrtb7e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706082803/http://www.mecs-press.org/ijisa/ijisa-v7-n9/IJISA-V7-N9-7.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e4/4d/e44d460b78f1ef1789b75d417a2bc1bd558c2952.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijisa.2015.09.07"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction

J.P. Kharat
<span title="2017-12-01">2017</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ojahcxzn5ja27dfxbw3yqgfqee" style="color: black;">International Journal of Advances in Applied Sciences</a> </i> &nbsp;
For that three types of neural architectures are used namely Feed forward, Cascaded Feed forward and Time Delay Neural Network.  ...  The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models.</p>  ...  The best performance we get for cascaded feed-forward and feed-forward neural network in terms of error measure.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijaas.v6.i4.pp283-292">doi:10.11591/ijaas.v6.i4.pp283-292</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/spabtmznwfdspcgqwe5so6vepu">fatcat:spabtmznwfdspcgqwe5so6vepu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307122221/http://ijaas.iaescore.com/index.php/IJAAS/article/download/9466/7874" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijaas.v6.i4.pp283-292"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Estimation of the Pre-Consolidation Pressure in Soils Using ANN method

M. R Motahari
<span title="2016-07-25">2016</span> <i title="Enviro Research Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rdnzrodhzjfhpjsy3p2cxbrpue" style="color: black;">Current World Environment</a> </i> &nbsp;
In the current paper, by using the MATLAB toolbox, a BP neural network based method is applied to calculate and model pre-consolidation pressure.  ...  Finally, the accuracy and effectiveness of the proposed method is compared to the common analytical method.  ...  In this research, to lower the error in getting the maximum curvature point a nonlinear neural network prediction model based on a successive approximation of multi-node is adopted for providing a new  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12944/cwe.11.special-issue1.10">doi:10.12944/cwe.11.special-issue1.10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nliddmawqbevdd2of42mq7mh4u">fatcat:nliddmawqbevdd2of42mq7mh4u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427082949/http://www.cwejournal.org/pdf/vol11noSpecial/CWE_Vol11_Spl(1)_p_83-88.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e5/f2/e5f2bae87761fb6503b06392ecbc1d9485ac33f1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12944/cwe.11.special-issue1.10"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

COUPLING SUPERVISED AND UNSUPERVISED TECHNIQUES IN TRAINING FEED-FORWARD NETS

CRIS KOUTSOUGERAS, GEORGE PAPADOURAKIS
<span title="">1992</span> <i title="World Scientific Pub Co Pte Lt"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bmgus4fa7vg37kpnbuixwilqf4" style="color: black;">International journal on artificial intelligence tools</a> </i> &nbsp;
The same formulation gives rise to analytic expressions of the goals of the adaptation and leads to a new method for the adaptation of feed-forward nets.  ...  A popular approach to training feed-forward nets is to treat the problem of adaptation as a function approximation and to use curve fitting techniques.  ...  We then introduce a new formulation of the training problem for feed-forward nets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/s0218213092000120">doi:10.1142/s0218213092000120</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gtbn6jdbzbeb7bhuidjff4b75e">fatcat:gtbn6jdbzbeb7bhuidjff4b75e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170810042755/http://www2.southeastern.edu/Academics/Faculty/ck/paps/coupl-sup-unsup.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/60/dc/60dcc94295072e16361d0732696e840d1149c021.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1142/s0218213092000120"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> worldscientific.com </button> </a>
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