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Neural Networks modeling and Control / Jorge D. Rios

Public ISBD Titre : Neural Networks modeling and Control Titre original : Applications for unknown nonlinear delayed systems in discrete time Type de document : Livre Auteurs : Jorge D. Rios, Auteur ; Alma Y. Alanis, Auteur ; Carlos Lopez-Franco, Auteur ; Nancy Arana-Daniel, Auteur Editeur : Academic Press Année de publication : 2020 Importance : 141 Langues : Anglais (eng) Index. décimale : 681.5.01 Principes et théorie de l'automation Résumé : Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends. Neural Networks modeling and Control = Applications for unknown nonlinear delayed systems in discrete time [Livre] / Jorge D. Rios, Auteur ; Alma Y. Alanis, Auteur ; Carlos Lopez-Franco, Auteur ; Nancy Arana-Daniel, Auteur . - Academic Press, 2020 . - 141. Langues : Anglais (eng) Index. décimale : 681.5.01 Principes et théorie de l'automation Résumé : Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends.

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
90492	681.5.01 JOR	Livre	ECAM	automatique	Disponible

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