Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Discretet-time Hammerstein systems -- Kernel algorithms -- Semirecursive kernel algorithms -- Recursive kernel algorithms -- Orthogonal series algorithms -- Algorithms with ordered observations -- Continuous-time Hammerstein systems -- Discrete-time Wiener systems -- Kernel and orthogonal series algorithms -- Continuous-time Wiener system -- Other block-oriented nonlinear systems -- Multivariate nonlinear block-oriented systems -- Semiparametric identification -- Convolution and kernel functions -- Orthogonal functions -- Probability and statistics.

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.