– An extensive discussion of system identification starting from a frequency domain formulation is given in
System Identification – A frequency Domain Approach – second edition, R. Pintelon and J. Schoukens (2012), IEEE Press, Wiley.
– A comprehensive description of system identification is given in
System Identification – Theory For the User – second edition, L. Ljung (1999), PTR Prentice Hall, Upper Saddle River, N.J.
System Identification, T. Söderström and P. Stoica (1989), Prentice Hall International, Hemel Hempstead.
– A learn-by-doing approach to system identification is given in
Mastering System Identification in 100 Exercises, J. Schoukens, R. Pintelon, and Y. Rolain (2012), IEEE Press, Wiley.
– A comprehensive introduction to nonlinear system identification, focusing on the intuitive understanding of the basic relationships is given in
Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes, O. Nelles (2020), Springer.
– A comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains using NARMAX methods is given in
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio–Temporal Domains, S.A. Billings (2013), John Wiley & Sons, Ltd.
– The frequency response and the impulse response functions are nonparametric models for a SISO linear system. An extensive overview, ranging from the classical approaches to the very recent methods is given in
Nonparametric Data-Driven Modeling of Linear Systems: Estimating the Frequency Response and Impulse Response Function,
J. Schoukens, K. Godfrey and M. Schoukens (2018), IEEE Control Systems Magazine, vol. 38, no. 4, pp. 49-88.
– A bird’s eye view of non-linear system identification is given in
Nonlinear System Identification: A User-Oriented Road Map, J. Schoukens and L. Ljung (2019), IEEE Control Systems Magazine, vol. 39, no. 6, pp. 28-99.
– How to identify a linear model if we know that nonlinear distortions are present is discussed in
Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation, J. Schoukens, M. Vaes and R. Pintelon (2016), IEEE Control Systems Magazine, vol. 36, no. 3, pp. 38-69.