Adaptive Learning Methods for Nonlinear System Modeling

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.



  • 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. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
  7. Acknowledgments
  8. Chapter 1: Introduction
    1. Abstract
    2. 1.1. Nonlinear System Modeling: Background, Motivation and Opportunities
    3. 1.2. Key Factors in Defining Adaptive Learning Methods for Nonlinear System Modeling
    4. 1.3. Book Organization
    5. 1.4. Further Readings
    6. References
  9. Part 1: Linear-in-the-Parameters Nonlinear Filters
    1. Chapter 2: Orthogonal LIP Nonlinear Filters
      1. Abstract
      2. 2.1. Introduction
      3. 2.2. LIP Nonlinear Filters
      4. 2.3. Recent Identification Methods for Orthogonal Filters
      5. 2.4. Experimental Results
      6. 2.5. Concluding Remarks
      7. References
    2. Chapter 3: Spline Adaptive Filters
      1. Abstract
      2. Acknowledgements
      3. 3.1. Introduction
      4. 3.2. Foundation of Spline Interpolation
      5. 3.3. Spline Adaptive Filters
      6. 3.4. Convergence Properties
      7. 3.5. Experimental Results
      8. 3.6. Conclusion
      9. References
    3. Chapter 4: Recent Advances on LIP Nonlinear Filters and Their Applications
      1. Abstract
      2. Acknowledgements
      3. 4.1. Introduction
      4. 4.2. A Concise Categorization of State-of-the-Art LIP Nonlinear Filters
      5. 4.3. Fundamental Methods for Coefficient Adaptation
      6. 4.4. Significance-Aware Filtering
      7. 4.5. Experiments and Evaluation
      8. 4.6. Outlook on Model Structure Estimation
      9. 4.7. Summary
      10. References
  10. Part 2: Adaptive Algorithms in the Reproducing Kernel Hilbert Space
    1. Chapter 5: Maximum Correntropy Criterion–Based Kernel Adaptive Filters
      1. Abstract
      2. 5.1. Introduction
      3. 5.2. Kernel Adaptive Filters
      4. 5.3. Maximum Correntropy Criterion
      5. 5.4. Kernel Adaptive Filters Under Generalized MCC
      6. 5.5. Simulation Results
      7. 5.6. Conclusion
      8. References
    2. Chapter 6: Kernel Subspace Learning for Pattern Classification
      1. Abstract
      2. 6.1. Introduction
      3. 6.2. Kernel Methods
      4. 6.3. Kernel Subspace Approximation
      5. 6.4. Adaptive Kernel Subspace Approximation Algorithm
      6. 6.5. Infrastructures
      7. 6.6. Conclusion
      8. Appendix 6.A.
      9. References
    3. Chapter 7: A Random Fourier Features Perspective of KAFs With Application to Distributed Learning Over Networks
      1. Abstract
      2. 7.1. Introduction
      3. 7.2. Approximating the Kernel
      4. 7.3. Online Kernel-Based Learning: A Random Fourier Features Perspective
      5. 7.4. Online Distributed Learning With Kernels
      6. 7.5. Conclusions
      7. References
    4. Chapter 8: Kernel-Based Inference of Functions Over Graphs
      1. Abstract
      2. Acknowledgements
      3. 8.1. Introduction
      4. 8.2. Reconstruction of Functions Over Graphs
      5. 8.3. Inference of Dynamic Functions Over Dynamic Graphs
      6. References
  11. Part 3: Nonlinear Modeling With Multiple Learning Machines
    1. Chapter 9: Online Nonlinear Modeling via Self-Organizing Trees
      1. Abstract
      2. Acknowledgements
      3. 9.1. Introduction
      4. 9.2. Self-Organizing Trees for Regression Problems
      5. 9.3. Self-Organizing Trees for Binary Classification Problems
      6. 9.4. Numerical Results
      7. Appendix 9.A.
      8. References
    2. Chapter 10: Adaptation and Learning Over Networks for Nonlinear System Modeling
      1. Abstract
      2. Acknowledgements
      3. 10.1. Introduction
      4. 10.2. Mathematical Formulation of the Problem
      5. 10.3. Existing Approaches to Nonlinear Distributed Filtering
      6. 10.4. A Distributed Kernel Filter for Multitask Problems
      7. 10.5. Experimental Evaluation
      8. 10.6. Discussion and Open Problems
      9. References
    3. Chapter 11: Combined Filtering Architectures for Complex Nonlinear Systems
      1. Abstract
      2. 11.1. Introduction
      3. 11.2. Nonlinear Adaptive Filters
      4. 11.3. Different Approaches to Combine Nonlinear Adaptive Filters
      5. 11.4. Combined Nonlinear Filters With Diversity in the Parameters
      6. 11.5. Combination Schemes to Simplify the Selection of the Filter Structure
      7. 11.6. Conclusions
      8. References
  12. Part 4: Nonlinear Modeling by Neural Systems
    1. Chapter 12: Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models
      1. Abstract
      2. Acknowledgements
      3. 12.1. Introduction
      4. 12.2. Mathematical Background
      5. 12.3. Quaternion ESNs
      6. 12.4. Simulations
      7. 12.5. Discussion and Conclusion
      8. References
    2. Chapter 13: Identification of Short-Term and Long-Term Functional Synaptic Plasticity From Spiking Activities
      1. Abstract
      2. Acknowledgements
      3. 13.1. Introduction
      4. 13.2. Identification of STSP With Nonlinear Dynamical Model
      5. 13.3. Identification of LTSP With Nonstationary Model
      6. 13.4. Identification of Synaptic Learning Rule
      7. 13.5. Summary and Discussion
      8. References
    3. Chapter 14: Adaptive H∞ Tracking Control of Nonlinear Systems Using Reinforcement Learning
      1. Abstract
      2. 14.1. Introduction
      3. 14.2. H∞ Optimal Tracking Control for Nonlinear Affine Systems
      4. 14.3. H∞ Optimal Tracking Control for a Class of Nonlinear Nonaffine Systems
      5. References
    4. Chapter 15: Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems
      1. Abstract
      2. Acknowledgements
      3. 15.1. Introduction
      4. 15.2. Problem Description
      5. 15.3. Model Reduction Based on KLD and Singular Perturbation Technique
      6. 15.4. Adaptive Optimal Control Design With NDP
      7. 15.5. Adaptive Optimal Control Based on Policy Iteration for Partially Unknown DPSs
      8. 15.6. Conclusions
      9. References
  13. Index

Product Information

  • Title: Adaptive Learning Methods for Nonlinear System Modeling
  • Author(s): Danilo Comminiello, Jose C. Principe
  • Release date: June 2018
  • Publisher(s): Butterworth-Heinemann
  • ISBN: 9780128129777