System Identification is a special section of the International Federation of Automatic Control (IFAC)-Journal Automatica that contains tutorial papers regarding the basic methods and procedures utilized for system identification. Topics include modeling and identification; step response and frequency response methods; correlation methods; least squares parameter estimation; and maximum likelihood and prediction error methods. After analyzing the basic ideas concerning the parameter estimation methods, the book elaborates on the asymptotic properties of these methods, and then investigates the application of the methods to particular model structures. The text then discusses the practical aspects of process identification, which includes the usual, general procedures for process identification; selection of input signals and sampling time; offline and on-line identification; comparison of parameter estimation methods; data filtering; model order testing; and model verification. Computer program packages are also discussed. This compilation of tutorial papers aims to introduce the newcomers and non-specialists in this field to some of the basic methods and procedures used for system identification.

This Proceedings contains the papers presented at the IFAC Symposium on System Identification held in Santa Barbara, USA, on 21-23 June 2000. SYSID is IFAC's longest running symposium, and has rightly earned a reputation as the most authoritative conference in the field of system identification. The Proceedings of SYSID 2000 lives up to this reputation, and contains over 200 important papers devoted to leading edge research in all aspects of system identification. Theoretical aspects of identification are covered by papers on a huge range of topics including robust control, subspace methods, model error modelling, adaptive control, state and parameter estimation, and non-linear identification. There are also many papers with application to such areas as biological systems, environmental systems, process control, and automotive control. A number of software demonstrations is also presented, with direct application to system identification problems.