In many scientific problems an essential step toward their solution is to accmplish modeling and identification of soma object or system under investigation. As defined here, system identification is the process of deriving mathematical system model from observed data in accodance with some predetermined criterion. The increasing expansion in the use of system identification is the result of demands imposed by advances in other scientific and technological areas such as biomedicine, physics, electrical engineering, and computer science.
Author(s): Rolf Johansson
Publisher: Prentice-Hall International
Year: 1993
1. Introduction
2. Black box models
3. Signals and systems
4. Spectrum analysis
5. Linear regression
6. Identification of time-series models
7. Modeling
8. The experimental procedure
9. Model validation
10. Model approximation
11. Real-time identification
12. Continous-time models
13. Multidimensional identification
14. Nonlinear system identification
15. Adaptive systems
A. Appendix: Basic matrix algebra
B. Appendix: Statistical inference
C. Appendix: Numerical optimization
D. Appendix: Statistical properties of time series
E. Appendix: A case study
