Adaptive Learning Methods for Nonlinear System Modeling - 1st Edition - ISBN: 9780128129760

Adaptive Learning Methods for Nonlinear System Modeling

1st Edition

Editors: Danilo Comminiello Jose Principe
Paperback ISBN: 9780128129760
Imprint: Butterworth-Heinemann
Published Date: 1st June 2018
Page Count: 300
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Description

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.

Key Features

  • 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

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 Filtering

PART 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 Graphs

PART 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 Systems

PART 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

Details

No. of pages:
300
Language:
English
Copyright:
© Butterworth-Heinemann 2018
Published:
Imprint:
Butterworth-Heinemann
Paperback ISBN:
9780128129760

About the Editor

Danilo Comminiello

Since 2008 Danilo Comminiello has been performing research on adaptive algorithms for nonlinear system modeling. His initial focus was on adaptive filtering, and he started developing novel algorithms and methods to address the identification of nonlinear systems requiring online processing, such as real-time audio applications (e.g., nonlinear acoustic echo cancellation, active noise control). The range of applications have been expanded to include not only audio applications but several nonlinear problems such as channel identification, wind prediction, vibration control, high power amplifier modeling, and loudspeaker modeling, among others.

Affiliations and Expertise

Department of Information Engineering, Electronics and Telecommunications - Sapienza University of Rome, Italy

Jose Principe

Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is BellSouth Professor and the Founding Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL). His primary research interests are in advanced signal processing with information theoretic criteria (entropy and mutual information) and adaptive models in reproducing kernel Hilbert spaces (RKHS), and the application of these advanced algorithms to Brain Machine Interfaces (BMI). Dr. Principe is a Fellow of the IEEE, ABME, and AIBME. He is the past Editor in Chief of the IEEE Transactions on Biomedical Engineering, past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, and Past-President of the International Neural Network Society. He received the IEEE EMBS Career Award, and the IEEE Neural Network Pioneer Award. He has more than 600 publications and 30 patents (awarded or filed).

Affiliations and Expertise

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA

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