System Identification and Data Modelling of Complex Nonlinear and Nonstationary Processes

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Many real-world systems in engineering and science are inherently nonlinear and nonstationary in nature. However, the analysis and modelling techniques commonly used to study many types of typically complex nonlinear and nonstationary processes often assume linearity and stationarity. Conventional signal processing techniques include non-parametric approaches (e.g. statistics and power spectral estimation) and linear parametric modelling approaches (e.g. linear time-invariant and simply time-varying models). It has been noticed that complex signals may contain hidden information which cannot be sufficiently revealed and characterised by using traditional analysis methods. This project is primarily aimed to investigate, adapt and develop system identification and data modelling methods and algorithms for the analysis of nonlinear and nonstationary complex dynamical processes in the time, frequency, time-frequency and spatio-temporal domains.