Non-Parametric System Identification

Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this books shows that non-parametric regression can be successfully applied to system identification, and it highlights the achievements in doing so. With emphasis on Hammerstein, Wiener systems, and their multidimensional extensions, the authors show how to identify nonlinear subsystems and their characteristics when limited information exists. Algorithms using trigonometric, Legendre, Laguerre, and Hermite series are investigated, and the kernel algorithm, its semirecursive versions, and fully recursive modifications are covered. The theories of modern non-parametric regression, approximation, and orthogonal expansions, along with new approaches to system identification (including semiparametric identification), are provided. Detailed information about all tools used is provided in the appendices. This book is for researchers and practitioners in systems theory, signal processing, and communications and will appeal to researchers in fields like mechanics, economics, and biology, where experimental data are used to obtain models of systems. "Presenting a thorough overview of the theoretical foundations of nonparametric systems identification for nonlinear block-oriented systems, Wlodzimierz Greblicki and Miroslaw Pawlak show that nonparametric regression can be successfully applied to system identification, and they highlight what you can achieve in doing so." This book is aimed at researchers and practitioners in systems theory, signal processing, and communications. It will also appeal to researchers in fields such as mechanics, economics, and biology, where experimental data are used to obtain models of systems. An overview of non-parametric system identification for nonlinear block-oriented systems for researchers and practitioners.