“Researchers will find this useful and rewarding.” (Journal of Statistical Computation and Simulation, September 2005)
“…a brief summary of the underlying mathematical theory and techniques used to identify, characterize, and elucidate linear and nonlinear physiological models.” (Computing Reviews.com, April 29, 2004)
“…a welcome reference for anyone involved in the study of nonlinear dynamic behavioral patterns of biomedical systems.” (IEEE Engineering in Medicine and Biology, January/February 2004)
“This book is excellent because it discusses in detail nearly all the useful techniques currently in use, and reveals their relative strengths and weaknesses.” (Annals of Biomedical Engineering, June 2004)
A comprehensive reference for nonlinear identification of biomedical systems
System identification encompasses a set of tools that construct mathematical models of dynamic systems from measurements of their inputs and outputs. Since many of the systems that are of interest to biomedical engineers and physiologists are nonlinear, mathematical models of nonlinear systems, and methods to construct them from experimental measurements, are required.
Identification of Nonlinear Physiological Systems presents the methods used to identify models of nonlinear systems from measurements in order to enable readers to make informed decisions regarding which techniques are likely to be most applicable to a given system or experiment. Providing both the theoretical background of the methods and practical advice on how to implement, apply, and interpret the results of these methods, the book:
- Reviews linear system models that are the bases for nonlinear models
- Develops nonlinear system models with an emphasis on the relationships between them
- Includes running MATLAB® examples to illustrate the results obtained by different methods when applied to the same data
- Details the relationships between various approaches and discusses their relative strengths and weaknesses
- Presents the results from several key studies employing system identification methods
Recent advances in such fields as high throughput genomics and proteomics and growing interest in the new paradigm of systems biology are making an understanding of nonlinear systems ever more urgent. Identification of Nonlinear Physiological Systems is a welcome reference for anyone involved in the study of the nonlinear dynamic behavior of biomedical systems.
From the Back Cover
A comprehensive reference for nonlinear identification of biomedical systems
System identification encompasses a set of tools that construct mathematical models of dynamic systems from measurements of their inputs and outputs. Since many of the systems that are of interest to biomedical engineers and physiologists are nonlinear, mathematical models of nonlinear systems, and methods to construct them from experimental measurements, are required.
Identification of Nonlinear Physiological Systems presents the methods used to identify models of nonlinear systems from measurements in order to enable readers to make informed decisions regarding which techniques are likely to be most applicable to a given system or experiment. Providing both the theoretical background of the methods and practical advice on how to implement, apply, and interpret the results of these methods, the book:
- Reviews linear system models that are the bases for nonlinear models
- Develops nonlinear system models with an emphasis on the relationships between them
- Includes running MATLAB® examples to illustrate the results obtained by different methods when applied to the same data
- Details the relationships between various approaches and discusses their relative strengths and weaknesses
- Presents the results from several key studies employing system identification methods
Recent advances in such fields as high throughput genomics and proteomics and growing interest in the new paradigm of systems biology are making an understanding of nonlinear systems ever more urgent. Identification of Nonlinear Physiological Systems is a welcome reference for anyone involved in the study of the nonlinear dynamic behavior of biomedical systems.
About the Author
David T. Westwick is an assistant professor in the Department of Electrical and Computer Engineering at the University of Calgary.
Robert E. Kearney is professor and Chair of the Department of Biomedical Engineering at McGill University. A recipient of the IEEE Millenium Medal, he is a Fellow of the IEEE and former President of the IEEE Engineering in Medicine and Biology Society.
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