Adaptive Learning Methods for Nonlinear System Modeling

ISBN-13: 9780128129760 / Angielski / Miękka / 2018 / 388 str.

Danilo Comminiello; Jose C. Principe

Recenzje

"This book is a joint work of an excellent international team of scientists working in the field of nonlinear signal processing and, in particular, designing adaptive filtering algorithms utilized in system identification and nonlinear system modeling."--Mathematical Reviews Clippings

Spis treści

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 H8 Tracking Control of Nonlinear Systems using Reinforcement Learning 15. Adaptive Dynamic Programming for Optimal Control of Nonlinear Distributed Parameter Systems