Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling discusses recent advances in adaptive algorithms and machine learning methods for nonlinear system modeling and identification. It focuses on online learning approaches, which process data example-by-example and allow to model complex nonlinearities, such as time-varying and dynamic behaviors. Possible fields of applications include distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data.

Format: Paperback / softback
Length: 388 pages
Publication date: 21 June 2018
Publisher: Elsevier - Health Sciences Division

Adaptive Learning Methods for Nonlinear System Modeling delves into the latest advancements in adaptive algorithms and machine learning techniques specifically tailored for nonlinear system modeling and identification. The reality of real-life problems necessitates a certain level of nonlinearity, rendering linear models inadequate. This book primarily concentrates on methodologies for nonlinear modeling that incorporate adaptive learning approaches to process data originating from unknown nonlinear systems. By leveraging available data, these methods strive to estimate the nonlinearity introduced by the unknown system. Specifically, the methods presented in this book are rooted in online learning approaches, which analyze data example by example and enable the modeling of even complex nonlinearities, such as exhibiting time-varying and dynamic behaviors. The potential applications of such algorithms span a wide range, including distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among numerous others. This book serves as an indispensable resource for researchers, Ph.D. and post-graduate students engaged in the fields of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. Furthermore, it holds appeal to the industry market and practitioners working with a diverse array of nonlinear systems.

Weight: 718g
Dimension: 190 x 234 x 26 (mm)
ISBN-13: 9780128129760