Cover for Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling

Book2018

Edited by:

Danilo Comminiello and José C. Príncipe

Adaptive Learning Methods for Nonlinear System Modeling

Book2018

 

Cover for Adaptive Learning Methods for Nonlinear System Modeling

Edited by:

Danilo Comminiello and José C. Príncipe

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Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system model ... read full description

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  2. Book chapterAbstract only

    Chapter 1 - Introduction

    Danilo Comminiello and José C. Príncipe

    Pages 1-11

  3. Book chapterNo access

    Index

    Pages 361-367

About the book

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.

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.

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.
  • 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.

Details

ISBN

978-0-12-812976-0

Language

English

Published

2018

Copyright

Copyright © 2018 Elsevier Inc. All rights reserved.

Imprint

Butterworth-Heinemann

Editors

Danilo Comminiello

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

José C. Príncipe

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