Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling introduces recent advances on adaptive algorithms and methods designed for nonlinear system modeling and identification. The book focuses on algorithms and methods that process data coming from an unknown nonlinear system. Such algorithms are based on an adaptive approach that allows the developer to estimate instant-by-instant (i.e., in an online manner) the nonlinearity introduced by the unknown system on the available data. This allows one to identify and model the unknown system, thus ensuring that the presence of nonlinearity in available data does not negatively affect performance.

Possible fields of the applications include, but are not limited to, Wireless Communications, Underwater Communications, Network Security, Nonlinear Modeling in Distributed Networks, Vehicular Networks, Active Noise Control, Information Forensics and Security and Nonlinear Modeling in Big Data, among others. This book is a valuable resource for researchers, PhD and post-graduate students, and those working in a variety of areas.

• Presents key trends and future perspectives in the field of nonlinear signal processing
• Provides some code for both methods and application scenarios
• Tackles state-of-the-art techniques in the very exciting area of online and adaptive nonlinear identification
• Helps users understand the most effective methods in non-linear system modeling, suggesting the right methodology to solve a particular problem