Nonparametric System Identification [Paperback]

An overview of non-parametric system identification for nonlinear block-oriented systems for researchers and practitioners.Provides an overview of non-parametric system identification for nonlinear block-oriented systems, as well as new approaches. Demonstrates possibilities of applying non-parametric regression to system identification and shows how to identify nonlinear subsystems and their characteristics with limited information. Ideal for researchers and practitioners in systems theory, signal processing, and communications and researchers in mechanics, economics, and biology.Provides an overview of non-parametric system identification for nonlinear block-oriented systems, as well as new approaches. Demonstrates possibilities of applying non-parametric regression to system identification and shows how to identify nonlinear subsystems and their characteristics with limited information. Ideal for researchers and practitioners in systems theory, signal processing, and communications and researchers in mechanics, economics, and biology.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 appendilƒ¼