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Design of a hierarchical fuzzy model predictive controller

Zeinab Fallah, Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab
<span title="2015-04-15">2015</span> <i title="Science Publishing Corporation"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/piy2nrvrjrfcfoz5nmre6zwa4i" style="color: black;">International Journal of Engineering &amp; Technology</a> </i> &nbsp;
In this paper, a hierarchical neuro-fuzzy model is used for nonlinear identification of the plant.  ...  Finally, a model predictive fuzzy controller based on a predictive cost function is proposed. Using Gradient Descent Algorithm, the parameters of the controller are optimized.  ...  There are different methods for solving non-convex non-linear optimization problems, among them methods like successive linearization of model equations, simultaneous model solution and optimization, and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14419/ijet.v4i2.2854">doi:10.14419/ijet.v4i2.2854</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wstmv3gzy5d7vo4lzffnupqax4">fatcat:wstmv3gzy5d7vo4lzffnupqax4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150506124808/http://www.sciencepubco.com:80/index.php/ijet/article/download/2854/1710" 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/ab/1e/ab1eebe5872afbbaf9ee0cd26d10746f5b5c21ac.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14419/ijet.v4i2.2854"> <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 Control of the Iron Ore Beneficiation Process Based on the Hammerstein Hybrid Model

Olga Porkuian, Vladimir Morkun, Natalia Morkun, Oleksandra Serdyuk
<span title="2019-12-01">2019</span> <i title="Walter de Gruyter GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qcagwivveveqtixkennvxb5wnu" style="color: black;">Acta Mechanica et Automatica</a> </i> &nbsp;
Non-linear, dynamic, non-stationary properties characterize objects of the iron ore beneficiation line. Therefore, for their approximation, it is advisable to use models of the Hammerstein class.  ...  Hence, it is recommended for the identification of beneficiation production objects.  ...  The proposed identification mechanisms based on Hammerstein's hybrid models allow us to avoid non-linear optimization with non-linear res, which greatly simplifies the process of model coefficients calculating  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2478/ama-2019-0036">doi:10.2478/ama-2019-0036</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b66pwheh7bak3cp2sb6jnlht4a">fatcat:b66pwheh7bak3cp2sb6jnlht4a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200316095051/https://content.sciendo.com/downloadpdf/journals/ama/13/4/article-p262.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/d4/fb/d4fbc33833f1daaa7045e2149b67f4b4ee9c1095.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2478/ama-2019-0036"> <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>

Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimization algorithm

Radisa Jovanovic, Vladimir Zaric
<span title="">2021</span> <i title="National Library of Serbia"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tmeepammybhehbbosy6lpmotly" style="color: black;">Thermal Science</a> </i> &nbsp;
This paper addressed two separate issues concerning model optimization and control. Firstly, the linear models for the three different operating points of the heat flow system were found.  ...  Moreover, the fuzzy controllers from the optimized and initial Takagi-Sugeno plant models are compared.  ...  In general, modeling rule-based TS fuzzy systems consists of two parts: structural modeling and parameter optimization.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/tsci210825324j">doi:10.2298/tsci210825324j</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uouja2bldrezpl6klvxtbzgkje">fatcat:uouja2bldrezpl6klvxtbzgkje</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211103184504/http://www.doiserbia.nb.rs/img/doi/0354-9836/2021%20OnLine-First/0354-98362100324J.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/bf/5a/bf5acb26723af1970c67d70a0e52e6efc26477c4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/tsci210825324j"> <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>

ON MULTI-OBJECTIVE IDENTIFICATION OF TAKAGI-SUGENO FUZZY MODEL PARAMETERS

Tor A. Johansen, Robert Babuska
<span title="">2002</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p3wtlm3j2fedxitkuu4bs3scau" style="color: black;">IFAC Proceedings Volumes</a> </i> &nbsp;
The problem of identifying the parameters of the constituent local linear models of Takagi-Sugeno fuzzy models is considered.  ...  In order to address the tradeoff between global model accuracy and interpretability of the local models as linearizations of a nonlinear system, two multi-objective identification algorithms are studied  ...  The weighting parameters β i ≥ 0 parameterize the set of Pareto-optimal solutions of the underlying multi-objective optimization problem, and essentially determine the tradeoff between the possibly conflicting  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3182/20020721-6-es-1901.00672">doi:10.3182/20020721-6-es-1901.00672</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5jefgtlxprbyplav57ca3ozqcu">fatcat:5jefgtlxprbyplav57ca3ozqcu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170921233408/http://folk.ntnu.no/torarnj/mixed_ifac.pdf?id=ansatte/Johansen_Tor.Arne/mixed_ifac.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/90/06/9006193ca6102f3ec9bde5b9abb5aa038bb10f81.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3182/20020721-6-es-1901.00672"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

On Multidimensional Linear Modelling Including Real Uncertainty

Jana Nowakova, Miroslav Pokorny
<span title="2014-12-31">2014</span> <i title="VSB Technical University of Ostrava, Faculty of Electrical Engineering and Computer Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s2e53o6nxbgczjcabahgyxamqi" style="color: black;">Advances in Electrical and Electronic Engineering</a> </i> &nbsp;
In the paper are defined vague data as specialized fuzzy sets -fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague regression parameters  ...  To identify the fuzzy coefficients of model the genetic algorithm is used. The linear approximation of vague function together with its possibility area are analytically and graphically expressed.  ...  Acknowledgment This work has been supported by Project SP2014/156, "Microprocessor based systems for control and measurement applications", of the Student Grant System, VSB-Technical University of Ostrava  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15598/aeee.v12i5.1143">doi:10.15598/aeee.v12i5.1143</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4oaitcurized7mefde7zf4i7hi">fatcat:4oaitcurized7mefde7zf4i7hi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200319113914/http://advances.utc.sk/index.php/AEEE/article/download/1143/1014" 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/9a/2e/9a2e35b25845cf2256bed7ffb23b8eb6a8305f16.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15598/aeee.v12i5.1143"> <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>

An ant colony optimization-based fuzzy predictive control approach for nonlinear processes

S. Bououden, M. Chadli, H.R. Karimi
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ozlq63ehnjeqxf6cuxxn27cqra" style="color: black;">Information Sciences</a> </i> &nbsp;
controller and adaptive fuzzy model predictive controller.  ...  In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed.  ...  On-line adaptive fuzzy identification is introduced to identify the system parameters.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2014.11.050">doi:10.1016/j.ins.2014.11.050</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/32ad4uavsjcm5i4dqlyqcbgiky">fatcat:32ad4uavsjcm5i4dqlyqcbgiky</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200318212738/https://isiarticles.com/bundles/Article/pre/pdf/46180.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/1c/39/1c39a150561a2974aea33871b03bbde01ed42fc1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2014.11.050"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Fuzzy modelling of a rotary dryer

Jukka Koskinen, Leena Yliniemi, Kauko Leiviskä
<span title="">2000</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p3wtlm3j2fedxitkuu4bs3scau" style="color: black;">IFAC Proceedings Volumes</a> </i> &nbsp;
The rule parameters are determined on the basis of clusters created by Kohonen learning rule method and the initial model is optimised by the trial and error method.  ...  In this research a fuzzy model is developed for a rotary dryer. It is applied to the pilot plant rotary dryer located in the Control Engineering Laboratory at Oulu University.  ...  The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1474-6670(17)36984-7">doi:10.1016/s1474-6670(17)36984-7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qte3qo4j5bbsvjom6ggryxc7t4">fatcat:qte3qo4j5bbsvjom6ggryxc7t4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829145720/http://jultika.oulu.fi/files/isbn951427511X.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/66/1a668ff38aab02d129ff2aae3a638e434ded2f93.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1474-6670(17)36984-7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

A Soft Computing Method for Efficient Modelling of Smart Cities Noise Pollution

Attila Nemes, Gyula Mester, Tibor Mester
<span title="">2018</span> <i title="Croatian Interdisciplinary Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cpoec3wm5fchrdgcuft6jqcwei" style="color: black;">Interdisciplinary Description of Complex Systems</a> </i> &nbsp;
are applied to Zadeh type fuzzy partition membership function parameters preliminary identification, and then gradient descent method is used for their fine-tuning optimization, while the fuzzy rule consequence  ...  linear parameters are calculated by singular value decomposition method to find the least squares optimal training data fitting of the model.  ...  Genetic algorithms are known powerful tools for global nonlinear search, thus suitable for efficient preliminary identification of fuzzy membership parameters [5] and also capable of fuzzy structure  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7906/indecs.16.3.1">doi:10.7906/indecs.16.3.1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zypqrhzq4rhz5adk7sf46rmxnm">fatcat:zypqrhzq4rhz5adk7sf46rmxnm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212112029/http://indecs.eu/2018/indecs2018-pp302-312.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/a9/78/a978b9883d9b8502a2fb46f2a7bdbf3fbffde836.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7906/indecs.16.3.1"> <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>

Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

Helbert Espitia, Ivan Machon, Hilario Lopez
<span title="">2021</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The application case consists of controlling a MIMO non-linear hydraulic system fed by a pump and a three-way valve.  ...  In this way, the plant identification and the controller optimization is performed iteratively.  ...  . • For the non-linear MIMO plant, the structure and initial configuration of the neuro-fuzzy systems used for identification and control are determined.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3073067">doi:10.1109/access.2021.3073067</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/cc545407eed248fa9898491699a263f5">doaj:cc545407eed248fa9898491699a263f5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kz76cbg6hbbnpdzdgnnkftitou">fatcat:kz76cbg6hbbnpdzdgnnkftitou</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210604080934/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09404180.pdf?tp=&amp;arnumber=9404180&amp;isnumber=9312710&amp;ref=" 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/6b/92/6b92b8ab5e1fd419c489b954ad996887be36f211.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3073067"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Hinging hyperplane based regression tree identified by fuzzy clustering and its application

Tamás Kenesei, János Abonyi
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/spnlkxfb7fevrarnlb7rv32roy" style="color: black;">Applied Soft Computing</a> </i> &nbsp;
A novel tool for regression tree identification is proposed based on the synergistic combination of fuzzy c-regression clustering and the concept of hierarchical modeling.  ...  In a special case (c = 2), fuzzy c-regression clustering can be used for identification of hinging hyperplane models.  ...  and has been applied for several decades for many purposes, therefore it is an exhaustively tested method in non-linear parameter and structure identification as well.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.asoc.2012.09.027">doi:10.1016/j.asoc.2012.09.027</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yksxhnrhnrbwxksncl2ndengoi">fatcat:yksxhnrhnrbwxksncl2ndengoi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190305043908/http://pdfs.semanticscholar.org/cf58/b2057a693b9110c676b33d756cb1a5fd4614.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/cf/58/cf58b2057a693b9110c676b33d756cb1a5fd4614.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.asoc.2012.09.027"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Computationally intelligent modeling and control of fluidized bed combustion process

Cojbasic Zarko, Nikolic Vlastimir, Ciric Ivan, Cojbasic Ljubica
<span title="">2011</span> <i title="National Library of Serbia"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tmeepammybhehbbosy6lpmotly" style="color: black;">Thermal Science</a> </i> &nbsp;
Also, efficient fuzzy non-linear fluidized bed combustion process modelling strategy by combining several linearized combustion models has been presented.  ...  Finally, fuzzy and conventional process control systems for fuel flow and primary air flow regulation based on developed models and optimized by genetic algorithms have also been developed.  ...  To develop models, the structure identification and parameter adjustment [8, 15, 24 ] tasks needed to be solved.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/tsci101205031c">doi:10.2298/tsci101205031c</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/djwuzlyi4rarzf6ahx5eckg3hu">fatcat:djwuzlyi4rarzf6ahx5eckg3hu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812165000/http://www.doiserbia.nb.rs/img/doi/0354-9836/2011/0354-98361100031C.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/c1/a8/c1a8fe5a3a38ee285386943aaa9e066d71cd6ae4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/tsci101205031c"> <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>

Fuzzy Logic based Autonomous Parking Systems – Part IV: A Multiple-Model Adaptive Neural-Fuzzy Controller [article]

Yu Wang, Xiaoxi Zhu
<span title="2019-11-07">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The proposed controller design incorporates the following control theorems -- non-linear system identification using neural network, fuzzy logic control, adaptive control as well as multiple models adaptation  ...  In this paper, a Multiple Models Adaptive Fuzzy Logic Controller (MM-AFLC) with Neural Network Identification is designed to control the unmanned vehicle in Intelligent Autonomous Parking System.  ...  As neural network is a generalized approach for plant identification, it can be applied to identify non-linear plant without knowing the actual model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.02703v1">arXiv:1911.02703v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/koxqfq7mkbfqpk3tb2uzpn7oqq">fatcat:koxqfq7mkbfqpk3tb2uzpn7oqq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200830100507/https://arxiv.org/pdf/1911.02703v1.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/93/d2/93d2eee09529dbfb65c50472f0940981aa29356e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.02703v1" 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>

A New Type of Adaptive Neural Network Fuzzy Controller in the Double Inverted Pendulum System [chapter]

Suying Zhang, Ran An, Shuman Shao
<span title="">2011</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In order to optimize and amend the front-part and later-part parameter of Takagi-Sugeno fuzzy model, a mixed algorithm of backward propagation (BP) and least square method (LSE) algorithm are used.  ...  A new type of adaptive neural network fuzzy controller based on the stability for the double inverted pendulum control problem is introduced.  ...  Fuzzy neural network are commonly used in non-linear, identification of unknown or nondeterministic system.  ... 
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Page 1096 of Mathematical Reviews Vol. 56, Issue 3 [page]

<span title="">1978</span> <i title="American Mathematical Society"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_mathematical-reviews" style="color: black;">Mathematical Reviews </a> </i> &nbsp;
Accordingly, the solution generated from such system models may be non-optimal.  ...  The fuzzy interpretive structural modeling methods are based on a fuzzy reachability matrix and fuzzy graphs. Panel members respond to a relational question “is s,Rs,?”  ... 
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Non-linear system modelling via online clustering and fuzzy support vector machines

Julio Cesar Tovar, Wen Yu
<span title="">2008</span> <i title="Inderscience Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zpgyhdwtjjgp3grrd6d25tejna" style="color: black;">International journal of Modeling, identification and control</a> </i> &nbsp;
This paper describes a novel non-linear modelling approach by online clustering, fuzzy rules and support vector machine.  ...  Structure identification is realised by an online clustering method and fuzzy support vector machines, and the fuzzy rules are generated automatically.  ...  The process of fuzzy rule extraction for non-linear systems modelling is called structure identification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1504/ijmic.2008.021088">doi:10.1504/ijmic.2008.021088</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vizhabs2czabfmx4kmtklmioqu">fatcat:vizhabs2czabfmx4kmtklmioqu</a> </span>
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