Arun K. Tangirala

Designed as a comprehensive classroom text on system identification - the book is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis. The text contains twenty-six chapters, organized into five parts for easy and progressive learning:

 

1. Part I: Introduction to Identification and Models for Deterministic Systems (Chaps. 1-6)

2. Part II: Models for Random Processes (Chaps. 7-11)

3. Part III: Estimation Theory (Chaps. 12-16)

4. Part IV: Identification of Dynamic Models - Concepts and Principles (Chaps. 17-24)

5. Part V: Advanced Topics (Chaps. 25-26)

 

While the focus is mostly on linear time-invariant systems, the chapters on advanced topics offer a tour of topics, namely, linear time-varying, non-linear and closed-loop identification using classical to modern ideas.

 

The book contains numerous solved, illustrated examples and case studies. MATLAB® codes based on the System Identification Toolbox (companion toolbox of MATLAB) are provided for all illustrated examples. Please follow the MATLAB Files in the dropdown menu above to obtain the MATLAB files for each chapter. End-of-chapter review questions and exercises makes it an ideal choice for the classroom setting. Solutions manual (for instructors, of course) can be obtained on request from the publisher. Needless to add, beginners and researchers will find the textbook useful in their practice.