Title page
by Jose C. Principe, Danilo Comminiello
Adaptive Learning Methods for Nonlinear System Modeling
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Acknowledgments
- Chapter 1: Introduction
- Part 1: Linear-in-the-Parameters Nonlinear Filters
- Chapter 2: Orthogonal LIP Nonlinear Filters
- Chapter 3: Spline Adaptive Filters
- Chapter 4: Recent Advances on LIP Nonlinear Filters and Their Applications
- Abstract
- Acknowledgements
- 4.1. Introduction
- 4.2. A Concise Categorization of State-of-the-Art LIP Nonlinear Filters
- 4.3. Fundamental Methods for Coefficient Adaptation
- 4.4. Significance-Aware Filtering
- 4.5. Experiments and Evaluation
- 4.6. Outlook on Model Structure Estimation
- 4.7. Summary
- References
- Part 2: Adaptive Algorithms in the Reproducing Kernel Hilbert Space
- Chapter 5: Maximum Correntropy Criterion–Based Kernel Adaptive Filters
- Chapter 6: Kernel Subspace Learning for Pattern Classification
- Chapter 7: A Random Fourier Features Perspective of KAFs With Application to Distributed Learning Over Networks
- Chapter 8: Kernel-Based Inference of Functions Over Graphs
- Part 3: Nonlinear Modeling With Multiple Learning Machines
- Part 4: Nonlinear Modeling by Neural Systems
- Chapter 12: Echo State Networks for Multidimensional Data: Exploiting Noncircularity and Widely Linear Models
- Chapter 13: Identification of Short-Term and Long-Term Functional Synaptic Plasticity From Spiking Activities
- Chapter 14: Adaptive H∞ Tracking Control of Nonlinear Systems Using Reinforcement Learning
- Chapter 15: Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems
- Abstract
- Acknowledgements
- 15.1. Introduction
- 15.2. Problem Description
- 15.3. Model Reduction Based on KLD and Singular Perturbation Technique
- 15.4. Adaptive Optimal Control Design With NDP
- 15.5. Adaptive Optimal Control Based on Policy Iteration for Partially Unknown DPSs
- 15.6. Conclusions
- References
- Index
Adaptive Learning Methods for Nonlinear System Modeling
First edition
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
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