This book is about prediction and control of processes which can be expressed by discrete-time models (i.e. the characteristics vary in some way with time). The aim of the book is to provide a unified and comprehensive coverage of the principles, perspectives and methods of adaptive prediction.
This book is about prediction and control of processes which can be expressed by discrete-time models (i.e. the characteristics vary in some way with time). The aim of the book is to provide a unified and comprehensive coverage of the principles, perspectives and methods of adaptive prediction.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Partha Pratim Kanjilal is an Associate Professor at the Indian Institute of Technology, Kharagpur. He is a graduate of the IIT and of the University of Sheffield. His research interests include modelling and prediction of complex processes, linear and nonlinear time-series analysis, signal processing and predictive control. He has a particular interest in the applications of orthogonal control. Dr Kanjilal has previously worked as a research fellow at Oxford University, CSIR and Tata Steel in India. He has published over 25 refereed papers in journals and international conferences.
Inhaltsangabe
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
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
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