Adaptive learning methods for nonlinear system modeling
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 n...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Kidlington, Oxford, United Kingdom
Butterworth-Heinemann, an imprint of Elsevier
2018
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| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Includes bibliographical references and index
- 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