Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimisation of error between the model response and actual system response. However, with the proliferation of highspeed digital computers, elegant and innovative techniques like filter error method, genetic algorithms and artificial neural networks are finding more and more use in parameter estimation problems. Modelling and Parameter Estimation of Dynamic Systems presents a detailed examination of many estimation techniques and modelling problems.

'while this book would be of interest to the general reader, it is of particular appeal to science and engineering students at all levels. It also presents a definite benefit to practising engineers and lecturers who are engaged in parameter estimation work. I found this an enthralling read, which now has a prominent place on my bookshelf - it certainly will be referred to in the future.'
-- Karl O. Jones Measurement & Control

• Chapter 1: Introduction
• Chapter 2: Least squares methods
• Chapter 3: Output error method
• Chapter 4: Filtering methods
• Chapter 5: Filter error method
• Chapter 6: Determination of model order and structure
• Chapter 7: Estimation before modelling approach
• Chapter 8: Approach based on the concept of model error
• Chapter 9: Parameter estimation approaches for unstable/augmented
• Chapter 10: Parameter estimation using artificial neural networks and genetic
• Chapter 11: Real-time parameter estimation
• Appendices