**Books**

– 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.

**Articles**

– 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.