The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.

Detalhes bibliográficos
Autor(a) principal: Rafael Fernandes Pinheiro
Data de Publicação: 2021
Tipo de documento: Tese
Idioma: eng
Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
Texto Completo: https://doi.org/10.11606/T.3.2021.tde-10022022-115037
Resumo: In this thesis, studies are presented that provide new sufficient conditions for the analysis of the absolute stability of Lurie type systems for the Single-Input-Single-Output (SISO) and Multiple-Input-Multiple-Output (MIMO) cases. From these new conditions obtained, controller designs for Lurie type systems are developed and applied to artificial neural networks. The results presented are based on the H control theory, using the mixed-sensitivity technique (S/KS/T) and the µ-analysis and synthesis technique. In addition, conditions for time-delay systems are also obtained. In the application, it is presented the model of a neuropathology that simulates memory loss using Hopfield networks in continuous time, which is called Alzheimer-like disease. Then, the developed controller, based on the theory of this work, is applied to correct the problem of memory failure. Examples are presented to illustrate the theory and simulations are performed to validate and show the effectiveness of the results.
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spelling info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease. O problema de Lurie e suas relações com redes neurais artificiais e doença tipo Alzheimer. 2021-12-01Diego ColónJosé Manoel BalthazarÁtila Madureira BuenoManuel Valentim de Pera GarciaJosé Roberto Castilho PiqueiraRafael Fernandes PinheiroUniversidade de São PauloEngenharia ElétricaUSPBR Alzheimer disease Análise µ Controle robusto DK-iteration Doença de Alzheimer Hopfield network Incertezas paramétricas Iteração DK Lurie problem Parametric uncertainty Problema de Lurie Redes de Hopfield Redes neurais Robust control µ-analysis In this thesis, studies are presented that provide new sufficient conditions for the analysis of the absolute stability of Lurie type systems for the Single-Input-Single-Output (SISO) and Multiple-Input-Multiple-Output (MIMO) cases. From these new conditions obtained, controller designs for Lurie type systems are developed and applied to artificial neural networks. The results presented are based on the H control theory, using the mixed-sensitivity technique (S/KS/T) and the µ-analysis and synthesis technique. In addition, conditions for time-delay systems are also obtained. In the application, it is presented the model of a neuropathology that simulates memory loss using Hopfield networks in continuous time, which is called Alzheimer-like disease. Then, the developed controller, based on the theory of this work, is applied to correct the problem of memory failure. Examples are presented to illustrate the theory and simulations are performed to validate and show the effectiveness of the results. Nesta tese, são apresentados estudos que fornecem novas condições suficientes para a análise da estabilidade absoluta de sistemas do tipo Lurie para os casos Single-Input Single-Output (SISO) e Multiple-Input-Multiple-Output (MIMO). Técnicas de projetos de controladores para sistemas tipo Lurie são desenvolvidas e aplicadas em redes neurais artificiais. Os resultados apresentados são baseados na teoria de controle H, sendo, para o caso SISO, usada a técnica de sensibilidade mista (S/KS/T) e para o caso MIMO utilizada a técnica de análise e síntese µ. Além disso, obtém-se condições para sistemas com atraso no tempo, mostrando que os resultados podem também ser utilizados quando existem atrasos. Na aplicação, primeiramente se faz a modelagem de uma neuropatologia que simula perda de memória usando redes de Hopfield em tempo contínuo, que é denominada neste trabalho como doença tipo-Alzheimer. Em seguida, o controlador desenvolvido, baseado na teoria deste trabalho, é aplicado para corrigir o problema de falha de memória. Exemplos são apresentados para ilustrar a teoria e simulações são realizadas para validar e mostrar a eficácia dos resultados. https://doi.org/10.11606/T.3.2021.tde-10022022-115037info:eu-repo/semantics/openAccessengreponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USP2023-12-21T18:35:19Zoai:teses.usp.br:tde-10022022-115037Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212023-12-22T12:25:16.586291Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false
dc.title.en.fl_str_mv The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
dc.title.alternative.pt.fl_str_mv O problema de Lurie e suas relações com redes neurais artificiais e doença tipo Alzheimer.
title The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
spellingShingle The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
Rafael Fernandes Pinheiro
title_short The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
title_full The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
title_fullStr The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
title_full_unstemmed The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
title_sort The Lurie problem and its relationships with artificial neural networks and alzheimer-like disease.
author Rafael Fernandes Pinheiro
author_facet Rafael Fernandes Pinheiro
author_role author
dc.contributor.advisor1.fl_str_mv Diego Colón
dc.contributor.referee1.fl_str_mv José Manoel Balthazar
dc.contributor.referee2.fl_str_mv Átila Madureira Bueno
dc.contributor.referee3.fl_str_mv Manuel Valentim de Pera Garcia
dc.contributor.referee4.fl_str_mv José Roberto Castilho Piqueira
dc.contributor.author.fl_str_mv Rafael Fernandes Pinheiro
contributor_str_mv Diego Colón
José Manoel Balthazar
Átila Madureira Bueno
Manuel Valentim de Pera Garcia
José Roberto Castilho Piqueira
description In this thesis, studies are presented that provide new sufficient conditions for the analysis of the absolute stability of Lurie type systems for the Single-Input-Single-Output (SISO) and Multiple-Input-Multiple-Output (MIMO) cases. From these new conditions obtained, controller designs for Lurie type systems are developed and applied to artificial neural networks. The results presented are based on the H control theory, using the mixed-sensitivity technique (S/KS/T) and the µ-analysis and synthesis technique. In addition, conditions for time-delay systems are also obtained. In the application, it is presented the model of a neuropathology that simulates memory loss using Hopfield networks in continuous time, which is called Alzheimer-like disease. Then, the developed controller, based on the theory of this work, is applied to correct the problem of memory failure. Examples are presented to illustrate the theory and simulations are performed to validate and show the effectiveness of the results.
publishDate 2021
dc.date.issued.fl_str_mv 2021-12-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.11606/T.3.2021.tde-10022022-115037
url https://doi.org/10.11606/T.3.2021.tde-10022022-115037
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade de São Paulo
dc.publisher.program.fl_str_mv Engenharia Elétrica
dc.publisher.initials.fl_str_mv USP
dc.publisher.country.fl_str_mv BR
publisher.none.fl_str_mv Universidade de São Paulo
dc.source.none.fl_str_mv reponame:Biblioteca Digital de Teses e Dissertações da USP
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Biblioteca Digital de Teses e Dissertações da USP
collection Biblioteca Digital de Teses e Dissertações da USP
repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)
repository.mail.fl_str_mv virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br
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