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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...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Kidlington, Oxford, United Kingdom :
Butterworth-Heinemann, an imprint of Elsevier,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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