Block-Oriented Identification of Nonlinear Systems

This book is intended to serve as a reference for advanced research in the area of nonlinear system identification specializing in electrical/mechanical/ chemical engineering. Hammerstein and Wiener models are two of the most widely used architectures for block-oriented nonlinear system identification. This book focuses on the identification of hammerstein and wiener models. The identification algorithms are developed based on radial basis functions neural networks. The alogrithms are supported by numerous simulations and convergence analysis.