System Identification and Data Modelling of Complex Nonlinear and Nonstationary Processes

工程和科学中的许多现实系统本质上是非线性和非平稳的。然而，通常用于研究许多类型的典型复杂非线性和非平稳过程的分析和建模技术通常假设线性和平稳。传统的信号处理技术包括非参数方法（如统计和功率谱估计）和线性参数建模方法（如线性时不变和简单时变模型）。人们已经注意到，复杂信号中可能含有传统分析方法无法充分揭示和表征的隐藏信息。本项目主要旨在研究、调整和发展系统识别和数据建模方法和算法，用于分析时间、频率、时频和时空域的非线性和非平稳复杂动态过程。

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.