Adaptive Learning Methods for Nonlinear System Modeling
Herausgeber: Comminiello, Danilo; Principe, Jose C.
Danilo Comminiello is a Tenure-Track Assistant Professor with the Department of Information Engineering, Electronics and Telecommunications (DIET) at Sapienza University of Rome, Italy, where he teaches Machine Learning for Signal Processing. His current research interests include computational intelligence and machine learning theory, particularly focused on audio and acoustic applications. Danilo Comminiello is a Senior Member of "Institute of Electrical and Electronics Engineers" (IEEE), and Member of "Audio Engineering Society" (AES) and "European Association for Signal Processing" (EURASIP). He is also a member of the "Task Force on Computational Audio Processing" of the IEEE "Intelligent System Applications" Technical Committee (IEEE Computational Intelligence Society).
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
