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Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques.
Effective system identification includes the underlying methodologies, computational procedures, and their implementation. To this end, this volume presents readers with the mathematical background required to participate in the growing field...
The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.
The book is organized in a reader-friendly way, starting with the simplest methods, and then gradually introducing more complex techniques. Thus, the reader is offered clear physical insight without recourse to large amounts of mathematics.
Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification.
This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems.
This valuable volume offers a systematic approach to flight vehicle system identification and exhaustively covers the time domain methodology. It addresses in detail the theoretical and practical aspects of various...
This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge.
J. Rissanen, “Recursive identification of linear systems,” SIAM J. Control, vol. 9, no. 3, pp. 420–430, 1971. Yu. A. Rozanov, Stationary Random Processes, Holden-Day, 1967. A. H. Sayed, Fundamentals ofAdaptive Filtering, Wiley, 2003.