Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this book 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.
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(253mm x 177mm x 31mm)
Cambridge University Press
Publisher: Cambridge University Press
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Author Biography - Wlodzimierz Greblicki
Wlodzimierz Greblicki is a professor at the Institute of Computer Engineering, Control, and Robotics at the Wroclaw University of Technology, Poland. Miroslaw Pawlak is a professor in the Department of Electrical and Computer Engineering at the University of Manitoba, Canada. He was awarded his PhD in 1982 from the Wroclaw University of Technology, Poland. Both authors have published extensively over the years in the area of non-parametric theory and applications.