2011, XXVI, 323p. 109 illus., 37 illus. in color. With online files/update.
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Uses a practical example-based approach to show students how identification really works
Teaches students the fundamentals of systems identification without unduly complicated mathematics
On-line solutions manual will assist the instructor with planning out-of-class assignments and already-practicing engineers with self-study
Takes account of the most recent developments in system identification
System Identification: an Introduction shows the (student) reader how to approach the system identification problem in a systematic fashion. Essentially, system identification is an art of modelling, where appropriate choices have to be made concerning the level of approximation, given prior system’s knowledge, noisy data and the final modelling objective. The system identification process is basically divided into three steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text.
The book contains four parts covering:
· data-based identification – non-parametric methods for use when prior system knowledge is very limited;
· time-invariant identification for systems with constant parameters;
· time-varying systems identification, primarily with recursive estimation techniques; and
· model validation methods.
The book uses essentially semi-physical or grey-box modelling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various model applications, as control, prediction and experimental design, with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from www.springer.com/978-0-85729-521-7) will both help students to assimilate what they have learnt and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques.
Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification: an Introduction will help academic instructors teaching control-related courses to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.
Content Level »Upper undergraduate
Keywords »Control Applications - Control Engineering - Dynamic Systems - Parameter Estimation - Parameter Estimation Textbook - System Identification - System Identification Textbook - Time Series