| Titre : |
Adaptive prediction and predictive control |
| Type de document : |
texte imprimé |
| Auteurs : |
Partha Pratim Kanjilal, Auteur |
| Editeur : |
London : The Institution of Electrical Engineers |
| Année de publication : |
1995 |
| Collection : |
IEE Control Engineering Series 52 |
| Importance : |
518 p. |
| Présentation : |
couv. ill. en coul., ill. |
| Format : |
24,4 cm. |
| ISBN/ISSN/EAN : |
978-0-86341-193-9 |
| Langues : |
Anglais (eng) |
| Catégories : |
AUTOMATISME
|
| Index. décimale : |
25-07 Théorie de la commande: commandes des processus |
| Résumé : |
Control often follows predictions: predictive control has been highly successful in producing robust and practical solutions in many real-life, real-time applications. Adaptive prediction covers a variety of ways of adding 'intelligence' to predictive control techniques. Many different groups, with widely varying disciplinary backgrounds and approaches, are tackling the same problem from different angles; these groups are sometimes unaware of alternative approaches from other disciplines.
This book attempts to give a unified and comprehensive coverage of the principles and methods that these groups have developed. It avoids basing its descriptions on very complex mathematical formulations but still gives a rigorous exposure to the subject, and illustrates the theory with many practical examples. It is chiefly aimed at students, researchers and practitioners, but will also be accessible to the non-specialist. |
| Note de contenu : |
contents:
Chapter 1: Introduction
Chapter 2: Process models
Chapter 3: Parameter estimation
Chapter 4: Some popular methods of prediction
Chapter 5: Adaptive prediction using transfer-function models
Chapter 6: Kalman filter and state-space approaches
Chapter 7: Orthogonal transformation and modelling of periodic series
Chapter 8: Modellong of nonlinear processes: an introduction
Chapter 9: Modelling of nonlinear processes using GMDH
Chapter 10: Modelling and prediction of nonlinear processes using neural networks
Chapter 11: Modelling and prediction of quasiperiodic series
Chapter 12: Predictive control (Part-I): input-output model based
Chapter 13: Predictive control (Part-II): state-space model based
Chapter 14: Smoothing and filtering
Appendices |
Adaptive prediction and predictive control [texte imprimé] / Partha Pratim Kanjilal, Auteur . - London : The Institution of Electrical Engineers, 1995 . - 518 p. : couv. ill. en coul., ill. ; 24,4 cm.. - ( IEE Control Engineering Series 52) . ISBN : 978-0-86341-193-9 Langues : Anglais ( eng)
| Catégories : |
AUTOMATISME
|
| Index. décimale : |
25-07 Théorie de la commande: commandes des processus |
| Résumé : |
Control often follows predictions: predictive control has been highly successful in producing robust and practical solutions in many real-life, real-time applications. Adaptive prediction covers a variety of ways of adding 'intelligence' to predictive control techniques. Many different groups, with widely varying disciplinary backgrounds and approaches, are tackling the same problem from different angles; these groups are sometimes unaware of alternative approaches from other disciplines.
This book attempts to give a unified and comprehensive coverage of the principles and methods that these groups have developed. It avoids basing its descriptions on very complex mathematical formulations but still gives a rigorous exposure to the subject, and illustrates the theory with many practical examples. It is chiefly aimed at students, researchers and practitioners, but will also be accessible to the non-specialist. |
| Note de contenu : |
contents:
Chapter 1: Introduction
Chapter 2: Process models
Chapter 3: Parameter estimation
Chapter 4: Some popular methods of prediction
Chapter 5: Adaptive prediction using transfer-function models
Chapter 6: Kalman filter and state-space approaches
Chapter 7: Orthogonal transformation and modelling of periodic series
Chapter 8: Modellong of nonlinear processes: an introduction
Chapter 9: Modelling of nonlinear processes using GMDH
Chapter 10: Modelling and prediction of nonlinear processes using neural networks
Chapter 11: Modelling and prediction of quasiperiodic series
Chapter 12: Predictive control (Part-I): input-output model based
Chapter 13: Predictive control (Part-II): state-space model based
Chapter 14: Smoothing and filtering
Appendices |
|  |
Adaptive prediction and predictive control
