
Adaptive Learning Methods for Nonlinear System Modeling
Description
Key Features
- 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.
Readership
Researcher, PhD and post-graduate students, industry market and practitioners working with any kind of nonlinear systems requiring an online processing
Table of Contents
1. Introduction
PART I – LINEAR-IN-THE-PARAMETERS NONLINEAR FILTERS
2. Orthogonal LIP Nonlinear Filters
3. Spline Adaptive Filters: Theory and Applications
4. Recent Advances on LIP Nonlinear Filters and Their Applications: Efficient Solutions and Significance Aware FilteringPART II – ADAPTIVE ALGORITHMS IN THE REPRODUCING KERNEL HILBERT SPACE
5. Maximum Correntropy Criterion Based Kernel Adaptive Filters
6. Kernel Subspace Learning for Pattern Classification
7. A Random Fourier Features Perspective of KAFs with Application to Distributed Learning over Networks
8. Kernel-based Inference of Functions over GraphsPART III – NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES
9. Online Nonlinear Modeling via Self-Organizing Trees
10. Adaptation and Learning Over Networks for Nonlinear System Modeling
11. Cooperative Filtering Architectures for Complex Nonlinear SystemsPART IV – NONLINEAR MODELING BY NEURAL NETWORKS
12. Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models
13. Identification of Short-Term and Long-Term Functional Synaptic Plasticity from Spiking Activities
14. Adaptive H∞ Tracking Control of Nonlinear Systems using Reinforcement Learning
15. Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems
Product details
- No. of pages: 388
- Language: English
- Copyright: © Butterworth-Heinemann 2018
- Published: June 11, 2018
- Imprint: Butterworth-Heinemann
- Paperback ISBN: 9780128129760
- eBook ISBN: 9780128129777
About the Editors
Danilo Comminiello
Affiliations and Expertise
Jose Principe
Affiliations and Expertise
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