Identification of Time-Varying Non-Linear Systems for Brain Connectivity Analysis

Many control systems encountered in physical, automobile engineering, economic phenomena and biomedical engineering fields are nonlinear and nonstationary to some extent. In general, nonlinear processes can be adequately characterized by a nonlinear model. Recently, a system can be obtained directly from experimental input/ output data by determining the system structure and the numerical values of the unknown parameters, this process is known as system identification. System identification techniques for linear and nonlinear systems have received such attention and have been widely applied to reveal fundamental properties of the system which are not apparent. Billings [1] surveyed the available approaches of non-linear system identification by considering the functional series of Volterra and Wiener, and the identification algorithms developed by Ku and Wolf [2]. Narendra and Parthasarathy [3] considered the orthogonal expansion methods and the kernel identification algorithms. All these methods discussed above were considered numerous alternatives and related topics which have been developed over the last decade or so.