| 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 / Partha Pratim Kanjilal
