Description
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes 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 many others.This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems.- Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning.- Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification.- Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Table of Contents
1. IntroductionPART I – LINEAR-IN-THE-PARAMETERS NONLINEAR FILTERS2. Orthogonal LIP Nonlinear Filters3. Spline Adaptive Filters: Theory and Applications4. Recent Advances on LIP Nonlinear Filters and Their Applications: Efficient Solutions and Significance Aware FilteringPART II – ADAPTIVE ALGORITHMS IN THE REPRODUCING KERNEL HILBERT SPACE5. Maximum Correntropy Criterion Based Kernel Adaptive Filters6. Kernel Subspace Learning for Pattern Classification7. A Random Fourier Features Perspective of KAFs with Application to Distributed Learning over Networks8. Kernel-based Inference of Functions over GraphsPART III – NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES9. Online Nonlinear Modeling via Self-Organizing Trees10. Adaptation and Learning Over Networks for Nonlinear System Modeling11. Cooperative Filtering Architectures for Complex Nonlinear SystemsPART IV – NONLINEAR MODELING BY NEURAL NETWORKS12. Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models13. Identification of Short-Term and Long-Term Functional Synaptic Plasticity from Spiking Activities14. Adaptive H∞ Tracking Control of Nonlinear Systems using Reinforcement Learning15. Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems
