Develops a systematic and a unified approach to the problem of physical system identification and its practical applications There is a need for a book which develops a systematic and a unified approach to the problem of physical system identification and its practical applications. Identification of Physical Systems addresses this need, developing identification theory using a coherent, simple and yet rigorous approach. Starting with a least-squares method, the author develops various schemes to address the issues of accuracy, variation in the operating regimes, closed loop and interconnected subsystems. He presents a non-parametric signal or data-based scheme to identify a system to provide a quick macroscopic picture of the system to complement the precise microscopic picture given by the parametric model based scheme. Finally, he develops a sequential integration of totally different schemes such as non-parametric, Kalman filter and parametric model to meet the speed and accuracy requirement of mission critical systems.Identification of Physical Systems includes case studies for the application of identification on physical laboratory scale systems, as well as number of illustrative examples throughout the book. * Provides a clear understanding of theoretical and practical issues in identification and its applications, enabling the reader to grasp a clear understanding of the theory and apply it to practical problems * Offers a self contained guide by including the background necessary to understand this interdisciplinary subject * Includes case studies for the application of identification on physical laboratory scale systems, as well as number of illustrative examples throughout the book * Applications of this methodology include the emerging areas of fault diagnosis, performance monitoring, condition-based maintenance, software-based sensors and autonomous systems. Primary: Graduate students and practicing engineers in electrical computer, biomedical, chemical and mechanical engineering. Secondary: Computer science students developing software for performance monitoring, fault diagnosis, condition-based monitoring or autonomous operation.