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.

'The book contains useful new material that can support postgraduate courses in the fields of control and signal processing. It can serve researchers and practicing engineers as a useful reference. On the whole, the book is an important contribution to the literature on the subject.'
-- IEEE Transactions on Automatic Control

• 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