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.
1.2 Systems and Models.
1.3 System Modeling.
1.4 System Identification.
1.5 How Common are Nonlinear Systems?
2.1 Vectors and Matrices.
2.2 Gaussian Random Variables.
2.3 Correlation Functions.
2.4 Mean–Square Parameter Estimation.
2.6 Notes and References.
2.8 Computer Exercises.
3. Models of Linear Systems.
3.1 Linear Systems.
3.2 Nonparametric Models.
3.3 Parametric Models.
3.4 State–Space Models.
3.5 Notes and References.
3.6 Theoretical Problems.
3.7 Computer Exercises.
4. Models of Nonlinear Systems.
4.1 The Volterra Series.
4.2 The Wiener Series.
4.3 Simple Block Structures.
4.4 Parallel Cascades.
4.5 The Wiener–Bose Model.
4.6 Notes and References.
4.7 Theoretical Problems.
4.8 Computer Exercises.
5. Identification of Linear Systems.
5.2 Nonparametric Time–Domain Models.
5.3 Frequency Response Estimation.
5.4 Parametric Methods.
5.5 Notes and References.
5.6 Computer Exercises.
6. Correlation–Based Methods.
6.1 Methods for Functional Expansions.
6.2 Block Structured Models.
6.4 Computer Exercises.
7. Explicit Least–Squares Methods.
7.2 The Orthogonal Algorithms.
7.3 Expansion Bases.
7.4 Principal Dynamic Modes.
7.6 Computer Exercises.
8. Iterative Least–Squares Methods.
8.1 Optimization Methods.
8.2 Parallel Cascade Methods.
8.3 Application: Visual Processing in the Light Adapted Fly Retina.
8.5 Computer Exercises.
IEEE Press Series in Biomedical Engineering.