Neural networks modeling and control: applications for unknown nonlinear delayed systems in discrete time
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)...
Gespeichert in:
| Beteiligte Personen: | , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Academic Press
2019
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780128170793/?ar |
| Zusammenfassung: | 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. Provide in-depth analysis of neural control models and methodologies Presents a comprehensive review of common problems in real-life neural network systems Includes an analysis of potential applications, prototypes and future trends. |
| Beschreibung: | <p>1. Introduction 2. Mathematical preliminaries 3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays 4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays 5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays 6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays 7. Concluding remarks and future trends</p> <p>Appendix A. Artificial neural networks B. Linear induction motor prototype C. Differential robot prototype</p>. - Description based on online resource, title from digital title page (viewed on December 21, 2020) |
| Umfang: | 1 Online-Ressource illustrations. |
| ISBN: | 9780128170793 0128170794 |
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| 520 | |a 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. Provide in-depth analysis of neural control models and methodologies Presents a comprehensive review of common problems in real-life neural network systems Includes an analysis of potential applications, prototypes and future trends. | ||
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| spelling | Rios, Jorge D. VerfasserIn aut Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time Jorge D. Rios, Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco Amsterdam Academic Press 2019 1 Online-Ressource illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p>1. Introduction 2. Mathematical preliminaries 3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays 4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays 5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays 6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays 7. Concluding remarks and future trends</p> <p>Appendix A. Artificial neural networks B. Linear induction motor prototype C. Differential robot prototype</p>. - Description based on online resource, title from digital title page (viewed on December 21, 2020) 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. Provide in-depth analysis of neural control models and methodologies Presents a comprehensive review of common problems in real-life neural network systems Includes an analysis of potential applications, prototypes and future trends. Neural networks (Computer science) Neural networks (Computer science) Industrial applications Discrete-time systems Discrete-time systems Industrial applications Réseaux neuronaux (Informatique) Réseaux neuronaux (Informatique) ; Applications industrielles Systèmes échantillonnés Systèmes échantillonnés ; Applications industrielles Neural networks (Computer science) ; Industrial applications Alanis, Alma Y. VerfasserIn aut Arana-Daniel, Nancy VerfasserIn aut Lopez-Franco, Carlos VerfasserIn aut 9780128170786 Erscheint auch als Druck-Ausgabe 9780128170786 |
| spellingShingle | Rios, Jorge D. Alanis, Alma Y. Arana-Daniel, Nancy Lopez-Franco, Carlos Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time Neural networks (Computer science) Neural networks (Computer science) Industrial applications Discrete-time systems Discrete-time systems Industrial applications Réseaux neuronaux (Informatique) Réseaux neuronaux (Informatique) ; Applications industrielles Systèmes échantillonnés Systèmes échantillonnés ; Applications industrielles Neural networks (Computer science) ; Industrial applications |
| title | Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time |
| title_auth | Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time |
| title_exact_search | Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time |
| title_full | Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time Jorge D. Rios, Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco |
| title_fullStr | Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time Jorge D. Rios, Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco |
| title_full_unstemmed | Neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time Jorge D. Rios, Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco |
| title_short | Neural networks modeling and control |
| title_sort | neural networks modeling and control applications for unknown nonlinear delayed systems in discrete time |
| title_sub | applications for unknown nonlinear delayed systems in discrete time |
| topic | Neural networks (Computer science) Neural networks (Computer science) Industrial applications Discrete-time systems Discrete-time systems Industrial applications Réseaux neuronaux (Informatique) Réseaux neuronaux (Informatique) ; Applications industrielles Systèmes échantillonnés Systèmes échantillonnés ; Applications industrielles Neural networks (Computer science) ; Industrial applications |
| topic_facet | Neural networks (Computer science) Neural networks (Computer science) Industrial applications Discrete-time systems Discrete-time systems Industrial applications Réseaux neuronaux (Informatique) Réseaux neuronaux (Informatique) ; Applications industrielles Systèmes échantillonnés Systèmes échantillonnés ; Applications industrielles Neural networks (Computer science) ; Industrial applications |
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