Identification of Nonlinear Physiological Systems

Preface. 1. Introduction. 1.1 Signals. 1.2 Systems and Models. 1.3 System Modeling. 1.4 System Identification. 1.5 How Common are Nonlinear Systems? 2. Background. 2.1 Vectors and Matrices. 2.2 Gaussian Random Variables. 2.3 Correlation Functions. 2.4 Mean-Square Parameter Estimation. 2.5 Polynomials. 2.6 Notes and References. 2.7 Problems. 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. 5.1 Introduction. 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. 6.3 Problems. 6.4 Computer Exercises. 7. Explicit Least-Squares Methods. 7.1 Introduction. 7.2 The Orthogonal Algorithms. 7.3 Expansion Bases. 7.4 Principal Dynamic Modes. 7.5 Problems. 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. 8.4 Problems 8.5 Computer Exercises. References. Index. IEEE Press Series in Biomedical Engineering.