Differential neural networks for robust nonlinear control :: identification, state estimation and trajectory tracking /
This volume deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
River Edge, NJ :
World Scientific,
©2001.
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| Online-Zugang: | Volltext |
| Zusammenfassung: | This volume deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical). |
| Beschreibung: | 1 online resource (xxxi, 422 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789812811295 981281129X 9810246242 9789810246242 1281956732 9781281956736 |
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| 520 | |a This volume deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical). | ||
| 505 | 0 | |a 0.1 Abstract ; 0.2 Preface ; 0.3 Acknowledgments ; 0.4 Introduction ; 0.4.1 Guide for the Readers ; 0.5 Notations ; I Theoretical Study ; 1 Neural Networks Structures ; 1.1 Introduction ; 1.2 Biological Neural Networks ; 1.3 Neuron Model. | |
| 505 | 8 | |a 1.4 Neural Networks Structures 1.4.1 Single-Layer Feedforward Networks ; 1.4.2 Multilayer Feedforward Neural Networks ; 1.4.3 Radial Basis Function Neural Networks ; 1.4.4 Recurrent Neural Networks ; 1.4.5 Differential Neural Networks ; 1.5 Neural Networks in Control. | |
| 505 | 8 | |a 1.5.1 Identification 1.5.2 Control ; 1.6 Conclusions ; 1.7 References ; 2 Nonlinear System Identification: Differential Learning ; 2.1 Introduction ; 2.2 Identification Error Stability Analysis for Simplest Differential Neural Networks without Hidden Layers. | |
| 505 | 8 | |a 2.2.1 Nonlinear System and Differential Neural Network Model 2.2.2 Exact Neural Network Matching with Known Linear Part ; 2.2.3 Non-exact Neural Networks Modelling: Bounded Unmodelled Dynamics Case. | |
| 505 | 8 | |a 2.2.4 Estimation of Maximum Value of Identification Error for Nonlinear Systems with Bounded Unmodelled Dynamics 2.3 Multilayer Differential Neural Networks for Nonlinear System On-line Identification ; 2.3.1 Multilayer Structure of Differential Neural Networks. | |
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| 700 | 1 | |a Sanchez, Edgar N. |0 http://id.loc.gov/authorities/names/no2002009402 | |
| 700 | 1 | |a Yu, Wen |c (Robotics engineer) |1 https://id.oclc.org/worldcat/entity/E39PCjB446T37xWG8pWTPFTHvd | |
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| author | Poznyak, Alexander S. |
| author2 | Sanchez, Edgar N. Yu, Wen (Robotics engineer) |
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| contents | 0.1 Abstract ; 0.2 Preface ; 0.3 Acknowledgments ; 0.4 Introduction ; 0.4.1 Guide for the Readers ; 0.5 Notations ; I Theoretical Study ; 1 Neural Networks Structures ; 1.1 Introduction ; 1.2 Biological Neural Networks ; 1.3 Neuron Model. 1.4 Neural Networks Structures 1.4.1 Single-Layer Feedforward Networks ; 1.4.2 Multilayer Feedforward Neural Networks ; 1.4.3 Radial Basis Function Neural Networks ; 1.4.4 Recurrent Neural Networks ; 1.4.5 Differential Neural Networks ; 1.5 Neural Networks in Control. 1.5.1 Identification 1.5.2 Control ; 1.6 Conclusions ; 1.7 References ; 2 Nonlinear System Identification: Differential Learning ; 2.1 Introduction ; 2.2 Identification Error Stability Analysis for Simplest Differential Neural Networks without Hidden Layers. 2.2.1 Nonlinear System and Differential Neural Network Model 2.2.2 Exact Neural Network Matching with Known Linear Part ; 2.2.3 Non-exact Neural Networks Modelling: Bounded Unmodelled Dynamics Case. 2.2.4 Estimation of Maximum Value of Identification Error for Nonlinear Systems with Bounded Unmodelled Dynamics 2.3 Multilayer Differential Neural Networks for Nonlinear System On-line Identification ; 2.3.1 Multilayer Structure of Differential Neural Networks. |
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| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:33Z |
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| spelling | Poznyak, Alexander S. http://id.loc.gov/authorities/names/n94048975 Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu. River Edge, NJ : World Scientific, ©2001. 1 online resource (xxxi, 422 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Includes bibliographical references and index. Print version record. This volume deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical). 0.1 Abstract ; 0.2 Preface ; 0.3 Acknowledgments ; 0.4 Introduction ; 0.4.1 Guide for the Readers ; 0.5 Notations ; I Theoretical Study ; 1 Neural Networks Structures ; 1.1 Introduction ; 1.2 Biological Neural Networks ; 1.3 Neuron Model. 1.4 Neural Networks Structures 1.4.1 Single-Layer Feedforward Networks ; 1.4.2 Multilayer Feedforward Neural Networks ; 1.4.3 Radial Basis Function Neural Networks ; 1.4.4 Recurrent Neural Networks ; 1.4.5 Differential Neural Networks ; 1.5 Neural Networks in Control. 1.5.1 Identification 1.5.2 Control ; 1.6 Conclusions ; 1.7 References ; 2 Nonlinear System Identification: Differential Learning ; 2.1 Introduction ; 2.2 Identification Error Stability Analysis for Simplest Differential Neural Networks without Hidden Layers. 2.2.1 Nonlinear System and Differential Neural Network Model 2.2.2 Exact Neural Network Matching with Known Linear Part ; 2.2.3 Non-exact Neural Networks Modelling: Bounded Unmodelled Dynamics Case. 2.2.4 Estimation of Maximum Value of Identification Error for Nonlinear Systems with Bounded Unmodelled Dynamics 2.3 Multilayer Differential Neural Networks for Nonlinear System On-line Identification ; 2.3.1 Multilayer Structure of Differential Neural Networks. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Nonlinear control theory. http://id.loc.gov/authorities/subjects/sh90000979 Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Commande non linéaire. Commande robuste. TECHNOLOGY & ENGINEERING Automation. bisacsh Neural networks (Computer science) fast Nonlinear control theory fast Robust control fast Sanchez, Edgar N. http://id.loc.gov/authorities/names/no2002009402 Yu, Wen (Robotics engineer) https://id.oclc.org/worldcat/entity/E39PCjB446T37xWG8pWTPFTHvd has work: Differential neural networks for robust nonlinear control (Text) https://id.oclc.org/worldcat/entity/E39PCGBCK6wBbFHGpff8FXgJwy https://id.oclc.org/worldcat/ontology/hasWork Print version: Poznyak, Alexander S. Differential neural networks for robust nonlinear control. River Edge, NJ : World Scientific, ©2001 9789810246242 (DLC) 2002275143 (OCoLC)50291236 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235826 Volltext |
| spellingShingle | Poznyak, Alexander S. Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / 0.1 Abstract ; 0.2 Preface ; 0.3 Acknowledgments ; 0.4 Introduction ; 0.4.1 Guide for the Readers ; 0.5 Notations ; I Theoretical Study ; 1 Neural Networks Structures ; 1.1 Introduction ; 1.2 Biological Neural Networks ; 1.3 Neuron Model. 1.4 Neural Networks Structures 1.4.1 Single-Layer Feedforward Networks ; 1.4.2 Multilayer Feedforward Neural Networks ; 1.4.3 Radial Basis Function Neural Networks ; 1.4.4 Recurrent Neural Networks ; 1.4.5 Differential Neural Networks ; 1.5 Neural Networks in Control. 1.5.1 Identification 1.5.2 Control ; 1.6 Conclusions ; 1.7 References ; 2 Nonlinear System Identification: Differential Learning ; 2.1 Introduction ; 2.2 Identification Error Stability Analysis for Simplest Differential Neural Networks without Hidden Layers. 2.2.1 Nonlinear System and Differential Neural Network Model 2.2.2 Exact Neural Network Matching with Known Linear Part ; 2.2.3 Non-exact Neural Networks Modelling: Bounded Unmodelled Dynamics Case. 2.2.4 Estimation of Maximum Value of Identification Error for Nonlinear Systems with Bounded Unmodelled Dynamics 2.3 Multilayer Differential Neural Networks for Nonlinear System On-line Identification ; 2.3.1 Multilayer Structure of Differential Neural Networks. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Nonlinear control theory. http://id.loc.gov/authorities/subjects/sh90000979 Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Commande non linéaire. Commande robuste. TECHNOLOGY & ENGINEERING Automation. bisacsh Neural networks (Computer science) fast Nonlinear control theory fast Robust control fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh90000979 http://id.loc.gov/authorities/subjects/sh99013330 https://id.nlm.nih.gov/mesh/D016571 |
| title | Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / |
| title_auth | Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / |
| title_exact_search | Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / |
| title_full | Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu. |
| title_fullStr | Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu. |
| title_full_unstemmed | Differential neural networks for robust nonlinear control : identification, state estimation and trajectory tracking / Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu. |
| title_short | Differential neural networks for robust nonlinear control : |
| title_sort | differential neural networks for robust nonlinear control identification state estimation and trajectory tracking |
| title_sub | identification, state estimation and trajectory tracking / |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Nonlinear control theory. http://id.loc.gov/authorities/subjects/sh90000979 Robust control. http://id.loc.gov/authorities/subjects/sh99013330 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Commande non linéaire. Commande robuste. TECHNOLOGY & ENGINEERING Automation. bisacsh Neural networks (Computer science) fast Nonlinear control theory fast Robust control fast |
| topic_facet | Neural networks (Computer science) Nonlinear control theory. Robust control. Neural Networks, Computer Réseaux neuronaux (Informatique) Commande non linéaire. Commande robuste. TECHNOLOGY & ENGINEERING Automation. Nonlinear control theory Robust control |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235826 |
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