Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation: Engineering Systems and Sustainability
Autor Allamaraju Subrahmanyam, Ganti Prasada Raoen Limba Engleză Hardback – 11 dec 2019
In this book: Identification of Continuous-Time Systems-Linear and Robust Parameter Estimation, Allamaraju Subrahmanyam and Ganti Prasada Rao consider CT system models that are linear in their unknown parameters and propose robust methods of estimation. This book complements the existing literature on the identification of CT systems by enhancing the secondary stage through linear and robust estimation.
In this book, the authors
- provide an overview of CT system identification,
- consider Markov-parameter models and time-moment models as simple linear-in-parameters models for CT system identification,
- bring them into mainstream model parameterization via basis functions,
- present a methodology to robustify the recursive least squares algorithm for parameter estimation of linear regression models,
- suggest a simple off-line error quantification scheme to show that it is possible to quantify error even in the absence of informative priors, and
- indicate some directions for further research.
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Specificații
ISBN-13: 9780367371432
ISBN-10: 036737143X
Pagini: 142
Ilustrații: 14 Tables, black and white; 19 Illustrations, black and white
Dimensiuni: 156 x 234 x 15 mm
Greutate: 0.34 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Engineering Systems and Sustainability
ISBN-10: 036737143X
Pagini: 142
Ilustrații: 14 Tables, black and white; 19 Illustrations, black and white
Dimensiuni: 156 x 234 x 15 mm
Greutate: 0.34 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Engineering Systems and Sustainability
Cuprins
1. Introduction and Overview 2. Markov Parameter Models 3. Time Moment Models 4. Robust Parameter Estimation 5. Error Quantification 6. Conclusions
Notă biografică
Allamaraju Subrahmanyam, Ganti Prasada Rao
Descriere
This book is devoted to identification of Continuous -Time (CT) models which are linear in parameters starting with Markov Parameter and Time Moment models. It explains other linear-in parameters models – transfer functions represented as Poisson, Lauerre, and Kautz series along with detailed linear and robust estimation aspects.