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About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks
dc.contributor.author | Irigoyen Gordo, Eloy | |
dc.contributor.author | Barragán, Antonio Javier | |
dc.contributor.author | Larrea, Mikel ![]() | |
dc.contributor.author | Andújar, José Manuel | |
dc.date.accessioned | 2024-02-08T10:27:48Z | |
dc.date.available | 2024-02-08T10:27:48Z | |
dc.date.issued | 2018-07-08 | |
dc.identifier.citation | Complexity 2018 : (2018) // Article ID 3671428 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10810/65325 | |
dc.description.abstract | This work presents a straightforward methodology based on Neural Networks (NN) which allows to obtain relevant dynamic information of unknown nonlinear systems. It provides an approach for cases in which the complex task of analyzing the dynamic behaviour of nonlinear systems makes it excessively challenging to obtain an accurate mathematical model. After reviewing the suitability of Multilayer Perceptrons (MLPs) as universal approximators to replace a mathematical model, the first part of this work presents a system representation using a model formulated with state variables which can be exported to a NN structure. Considering the linearization of the NN model in a mesh of operating points, the second part of this work presents the study of equilibrium states in such points by calculating the Jacobian of the system through the NN model. The results analyzed in three case studies provide representative examples of the strengths of the proposed method. Conclusively, it is feasible to study the system behaviour based on MLPs, which enables the analysis of the local stability of the equilibrium points, as well as the system dynamics in its environment, therefore obtaining valuable information of the system dynamic behaviour. | es_ES |
dc.description.sponsorship | The authors would like to thank the Ministry of Economy, Industry and Competitiveness of Spain that has funded this work under the project DPI2017-85540-R (H2SMART- µGRID). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Marek Reformat | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.en | * |
dc.subject | dynamic analysis | es_ES |
dc.subject | stability | es_ES |
dc.subject | equilibrium state | es_ES |
dc.subject | linearization | es_ES |
dc.subject | neural model | es_ES |
dc.subject | nonlinear systems | es_ES |
dc.subject | MLP neural network | es_ES |
dc.title | About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2018 Eloy Irigoyen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | * |
dc.relation.publisherversion | https://www.hindawi.com/journals/complexity/2018/3671428/ | |
dc.identifier.doi | 10.1155/2018/3671428 | |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |
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Bestelakorik adierazi ezean, itemaren baimena horrela deskribatzen da:© 2018 Eloy Irigoyen et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.