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A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation

<span title="2017-05-18">2017</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ltuemt6stzfvldwwwy3crx2o74" style="color: black;">Materials</a> </i> &nbsp;
In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and  ...  The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability.  ...  Radial Basis Function Network Radial basis function network (RBFN) is one of the MLAs using multi-dimensional spatial interpolation technique.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ma10050543">doi:10.3390/ma10050543</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28772906">pmid:28772906</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5459084/">pmcid:PMC5459084</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzh3kni46ndsjjwswgsvizlee4">fatcat:wzh3kni46ndsjjwswgsvizlee4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180727234216/https://res.mdpi.com/def50200958b90cb6993cb69d40ae4f0444d453fb5ae75f75a1cb3d86ebb2be261e3f928f1e35c91d81a26679c02d2633a7f1e8c29390c02ea121f9bb5dfca8e49ed60ab3f8616dcdcc7d2470980853959155873d2869d66a0782197587f6a8c9ca971d36688a4bea560352486a6e3e4eada479d8dac9a90350368ebf0ffb91e60fef20deda6570158d026c234a4a28bfad4ad89609c?filename=&amp;attachment=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/5b/6b/5b6b93bf8963d5faab01b718df6ebec8faffe65b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ma10050543"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459084" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

Derya Avci, Akif Dogantekin
<span title="">2016</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ho44hu6idnfvhmsg63sv7jwk2i" style="color: black;">Parkinson&#39;s Disease</a> </i> &nbsp;
The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method.  ...  In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel.  ...  In these classic ELM classifiers, each of sigmoid, tangent sigmoid, triangular basis, radial basis, hard limit, and polykernel functions is used as the kernel function, respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2016/5264743">doi:10.1155/2016/5264743</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27274882">pmid:27274882</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4871978/">pmcid:PMC4871978</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e7jy6xysrjckbiuk5antromrv4">fatcat:e7jy6xysrjckbiuk5antromrv4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190226153631/http://pdfs.semanticscholar.org/7349/4d131232646b74c01426ae8e30ab19e32b58.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/73/49/73494d131232646b74c01426ae8e30ab19e32b58.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2016/5264743"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871978" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

10.5937/sjm9-5520 = On robust information extraction from high-dimensional data

Jan Kalina
<span title="">2014</span> <i title="Centre for Evaluation in Education and Science (CEON/CEES)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nky5kxvbobbpnkkzecp6nk6e6u" style="color: black;">Serbian Journal of Management</a> </i> &nbsp;
Acknowledgements The work was supported by the Czech Science Foundation project No. 13-01930S (Robust methods for nonstandard situations, their diagnostics and implementations).  ...  Radial Basis Function Network A radial basis function network is able to model a continuous nonlinear function.  ...  , radial basis function networks, self-organizing maps, and support vector machines.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5937/sjm9-5520">doi:10.5937/sjm9-5520</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vuzzfbopnnchddwxslzla3xxw4">fatcat:vuzzfbopnnchddwxslzla3xxw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20140616215412/http://www.sjm06.com:80/SJM%20ISSN1452-4864/9_1_2014_May_1-144/9_1_2014_131-144.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/74/4c/744c8d8182fc16456d634a98ff573cbcfd738bdb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5937/sjm9-5520"> <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>

Hybrid Approach to Optimize the Centers of Radial Basis Function Neural Network Using Particle Swarm Optimization

Monir Foqaha
<span title="">2017</span> <i title="International Academy Publishing (IAP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3uo3zcmrgvgspdzqe6w6sjoezq" style="color: black;">Journal of Computers</a> </i> &nbsp;
The aim of the proposed approach is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm.  ...  One of the important types of NNs is the Radial Basis Function Neural Networks (RBFN), which are characterized from other types of NNs, including better approximation, simpler network structures and faster-learning  ...  Radial Basis Function Neural Networks (RBFN) Radial Basis Function Neural Networks are type of neural networks whose activation functions in the hidden layer are radially symmetrical, which means its output  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17706/jcp.12.5.396-407">doi:10.17706/jcp.12.5.396-407</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7yqvdhhqyvevljav3ekujl3th4">fatcat:7yqvdhhqyvevljav3ekujl3th4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171114080200/http://www.jcomputers.us/vol12/jcp1205-03.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/5e/f6/5ef6583f3e29b089a610723059be982bb08bd6fc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17706/jcp.12.5.396-407"> <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>

PREDICTIVE MODELLING AND ANALYTICS FOR DIABETES USING A MACHINE LEARNING APPROACH

Prateek Mishra, Dr.Anurag Sharma, Dr. Abhishek Badholia
<span title="2021-02-28">2021</span> <i title="Auricle Technologies, Pvt., Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ti2mgqbqpfhhzla5hdk62fee2y" style="color: black;">Information Technology in Industry</a> </i> &nbsp;
algorithms namely multifactor dimensionality reduction (MDR), k-nearest neighbor (k-NN), artificial neural network (ANN) radial basis function (RBF) kernel support vector machine and linear kernel support  ...  With the use of R data manipulation tool analysis and development five different predictive models is done for the categorization of patients into diabetic and non- diabetic. supervised machine learning  ...  neural network (ANN), k-nearest neighbor (k-NN), radial basis kernel support vector machine (SVM-RBF) and linear kernel support vector machine (SVM-linear).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17762/itii.v9i1.121">doi:10.17762/itii.v9i1.121</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fhcj3de3ujcq7o3ie6yr6lojmm">fatcat:fhcj3de3ujcq7o3ie6yr6lojmm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210326133608/http://it-in-industry.org/index.php/itii/article/download/121/107" 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/53/8b/538bf2dad3bb4dfddca4a2d53df5f019bf588f5c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17762/itii.v9i1.121"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Intelligent Fault Diagnosis of Delta 3D Printers using Attitude Sensors based on Extreme Learning Machines

Li Xiaoyan, Guo Jianwen, Jia Xuejun, Zhang Shaohui, Liu Zhiyuan
<span title="">2019</span> <i title="Totem Publisher, Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7jcg35zmwbdmpmvxhyrwzy26s4" style="color: black;">International Journal of Performability Engineering</a> </i> &nbsp;
In order to study the fault diagnosis technique of delta 3D printers using extreme learning machine (ELM), a low-cost attitude sensor was used in our designed machine.  ...  The sin function, mexihat function, and tribas function recognition effects were better.  ...  Methodology Base of Extreme Learning Machine (ELM) Extreme learning machine (ELM) is a kind of progressive learning machine algorithm designed for single layer feedforward neuron networks (SLFN) [25  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23940/ijpe.19.12.p11.31963208">doi:10.23940/ijpe.19.12.p11.31963208</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ofggvtb55bhrbhsvxd4dtqwod4">fatcat:ofggvtb55bhrbhsvxd4dtqwod4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716071230/http://www.ijpe-online.com/EN/article/downloadArticleFile.do?attachType=PDF&amp;id=4313" 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/84/44/84448c35e4f4b84e8d59e893ce87c18b3f7d30d4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23940/ijpe.19.12.p11.31963208"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Performance Analysis of SVM Classification Model for Diagnosis of Alzheimer's Disease

Rajasree R.S., S. Brintha Rajakumari, Gajanan Babhulkar, Madhuri Gurale
<span title="2021-03-26">2021</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;
Alzheimer's disease (AD) is a type of Dementia which affects the brain and causes memory loss. It disrupts a person's ability to function independently.  ...  In our work, we have proposed a classification model using SVM model and anlaysed the performance of SVM model for different kernel methods.  ...  Linear and nonlinear radial basis function (RBF) kernels are widely used SVM kernels. a)Kernel Methods in SVM Linear Kernel It is used when data is linearly separable.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2021921144">doi:10.5120/ijca2021921144</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hnhp6z5cnbdojko6s6qu47o2hu">fatcat:hnhp6z5cnbdojko6s6qu47o2hu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210411122822/https://www.ijcaonline.org/archives/volume174/number27/rajasree-2021-ijca-921144.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/0a/9b/0a9bf6f411c8db0730b605ae9674391c40ffcfbb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2021921144"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method

Huihui Yu, Yingyi Chen, ShahbazGul Hassan, Daoliang Li
<span title="2016-06-08">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
The radial basis function neural network (RBFNN) method is one of the artificial neural network methods used for multi-sensor data fusion that has high accuracy 11 .  ...  To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector  ...  The work in this paper was supported by the National Natural Science Foundation Framework Project (No. 61571444) and the National Natural Science Foundation of China (61471133).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/srep27292">doi:10.1038/srep27292</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27270206">pmid:27270206</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4897606/">pmcid:PMC4897606</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iz4b5rqchrcd7ds5svar5ernfy">fatcat:iz4b5rqchrcd7ds5svar5ernfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190308031114/http://pdfs.semanticscholar.org/ebd1/141a5c162c4716a937396f0f379243dbd365.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/eb/d1/ebd1141a5c162c4716a937396f0f379243dbd365.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/srep27292"> <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> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897606" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Novel Comparison of Machine Learning Techniques for Predicting Photovoltaic Output Power

<span title="2021-09-01">2021</span> <i title="International Journal of Renewable Energy Research"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t4kj4w4wsnejjp2qgasd4ca6wa" style="color: black;">International Journal of Renewable Energy Research</a> </i> &nbsp;
The key contribution of this research is the comprehensive assessment of six complex machine learning techniques, using two years of input data, the most popular error metrics: R-squared, Root Mean Square  ...  Therefore, a performance comparison of several up-to-date machine learning algorithms is conducted in this paper for the hourly prediction of the resulting Photovoltaic power.  ...  In the literature several kernel functions, namely Gaussian, radial basis, polynomial, and linear, have been employed. In this study, a Radial Based Kernel Function is used [15] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20508/ijrer.v11i3.12056.g8252">doi:10.20508/ijrer.v11i3.12056.g8252</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ha54je4yxfc3ro77usonqda2se">fatcat:ha54je4yxfc3ro77usonqda2se</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220310062456/https://ijrer.org/ijrer/index.php/ijrer/article/download/12056/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/c9/dc/c9dc9abbbc7928990a7d7a349458d8dc924a4899.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20508/ijrer.v11i3.12056.g8252"> <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>

Radial Basis Function Process Neural Network Training Based on Generalized FRECHET Distance And GA-SA Hybrid Strategy

Wang Bing, Meng Yao-hua, Yu Xiao-hong
<span title="2013-12-31">2013</span> <i title="Academy and Industry Research Collaboration Center (AIRCC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cbdqntzqqjap5lbfx6xorcgofa" style="color: black;">Computer Science &amp; Engineering An International Journal</a> </i> &nbsp;
For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization training method based on GA combined with SA is proposed in this paper.  ...  Through building generalized Fr\'echet distance to measure similarity between time-varying function samples, the learning problem of radial basis centre functions and connection weights is converted into  ...  It also has certain reference value for other machine learning and complex function optimization problems. ( ( ), ( ),..., ( )) Figure 1 . 1 Radial centre function vector of a radial basis function neuron  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/cseij.2013.3601">doi:10.5121/cseij.2013.3601</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oe764uvjdnhenkbfn24bt3htiy">fatcat:oe764uvjdnhenkbfn24bt3htiy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180602012541/http://www.airccse.org/journal/cseij/papers/3613cseij01.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/1b/e6/1be69a25484bd279800e4a69e0231fdad6bb5eb5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/cseij.2013.3601"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

MULTILEVEL NONLINEAR MIXED-EFFECTS MODEL AND MACHINE LEARNING FOR PREDICTING THE VOLUME OF Eucalyptus SPP. TREES

Daniel Dantas, Natalino Calegario, Fausto Weimar Acerbi, Samuel de Pádua Chaves Carvalho, Marcos Antonio Isaac Júnior, Elliezer de Almeida Melo
<span title="">2020</span> <i title="FapUNIFESP (SciELO)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2laugz5rx5b6hmuvwxgagxvhnu" style="color: black;">Cerne</a> </i> &nbsp;
Multilevel nonlinear mixed-effects model and machine learning for predicting the volume of Eucalyptus spp. trees. CERNE, v. 26, n. 1, p. 48-57, 2020.  ...  Machine learning techniques are suitable in modeling tree volume. It was extracted accurate volume equation from neural network training process.  ...  The type IV error function, also known as epsregression, was used, and the Kernel function was a radial basis function (RBF).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1590/01047760202026012668">doi:10.1590/01047760202026012668</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xqnyn6q3obbvxekobzkqvdysnu">fatcat:xqnyn6q3obbvxekobzkqvdysnu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200621015227/https://www.scielo.br/pdf/cerne/v26n1/2317-6342-cerne-26-01-48.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/1f/5c/1f5c8a249fdab6f6b51e2840b155a74bbbfbcfd0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1590/01047760202026012668"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> scielo.br </button> </a>

Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization

Ting Liu, Qinwei Fan, Qian Kang, Lei Niu
<span title="2020-12-01">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vt2hc3xcijfofb7cwnxv4hhszi" style="color: black;">Processes</a> </i> &nbsp;
Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification  ...  Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root  ...  Acknowledgments: We are grateful to the editor and reviewers for their insightful comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr8121583">doi:10.3390/pr8121583</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ushmzk2unzaonfiajtrkqisz5i">fatcat:ushmzk2unzaonfiajtrkqisz5i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428144450/https://res.mdpi.com/d_attachment/processes/processes-08-01583/article_deploy/processes-08-01583.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/5c/36/5c36eb083e83a157849ac310da0146c93a55668e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr8121583"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Innovative Research on the Construction of Learner's Emotional Cognitive Model in E-Learning by Big Data Analysis

Hua Yin, Hong Wu, Sang-Bing Tsai
<span title="">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis  ...  In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds  ...  functions, and the radial basis kernel function has better learning ability, so this article uses this kind of kernel function.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/1460172">doi:10.1155/2021/1460172</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/6bdeb845b9cb43f6b1575615dca54cea">doaj:6bdeb845b9cb43f6b1575615dca54cea</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hgjhhmnztvhrzpnnmfbdwvnmay">fatcat:hgjhhmnztvhrzpnnmfbdwvnmay</a> </span>
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Study on a Novel Short-Term Load Forecasting Method Based on Improved PSO and FRBFNN

Yang Liu
<span title="2016-06-30">2016</span> <i title="Science and Engineering Research Support Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ym6wyoim55h6lliru6w4ssplki" style="color: black;">International Journal of Signal Processing, Image Processing and Pattern Recognition</a> </i> &nbsp;
In order to accurately, fast and efficiently forecast the short-term load of power system, an improved particle swarm optimization algorithm is proposed to optimize the parameters of fuzzy radial basis  ...  function fuzzy neural network(FRBFNN) model in order to train the FRBFNN model for obtaining the optimized FRBFNN(IWPSRFN) method.  ...  Ko and Lee [22] presented a hybrid algorithm which combines SVR (support vector regression), RBFNN (radial basis function neural network), and DEKF (dual extended Kalamn filter) to construct a prediction  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14257/ijsip.2016.9.6.22">doi:10.14257/ijsip.2016.9.6.22</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7a3dvyynpnd6nle2sptefnlyta">fatcat:7a3dvyynpnd6nle2sptefnlyta</a> </span>
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Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning

Allavikutty Raja, Sai Teja Chukka, Rengaswamy Jayaganthan
<span title="2020-10-09">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/54rwmy3lgfe7fbkfa7e74fa6fi" style="color: black;">Metals</a> </i> &nbsp;
In the present work, three machine algorithms such as extreme learning machine (ELM), back propagation neural networks (BPNN) and curve fitting model are implemented to analyse FCGR of Al alloys.  ...  Various machine learning techniques developed recently provide a flexible and adaptable approach to explain the complex mathematical relations especially, non-linear functions.  ...  [21] compared three ML-based algorithms, namely, extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimised back propagation network (GABP) to predict FCG  ... 
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