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...
| Otros Autores: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Kidlington, Oxford, United Kingdom :
Butterworth-Heinemann, an imprint of Elsevier
[2018]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630690206719 |
Tabla de Contenidos:
- Note continued: 8.2.4.Semiparametric Reconstruction
- 8.2.5.Numerical Tests
- 8.3.Inference of Dynamic Functions Over Dynamic Graphs
- 8.3.1.Kernels on Extended Graphs
- 8.3.2.Multikernel Kriged Kalman Filters
- 8.3.3.Numerical Tests
- 8.3.4.Summary
- Acknowledgments
- References
- pt. 3 NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES
- ch. 9 Online Nonlinear Modeling via Self-Organizing Trees
- 9.1.Introduction
- 9.2.Self-Organizing Trees for Regression Problems
- 9.2.1.Notation
- 9.2.2.Construction of the Algorithm
- 9.2.3.Convergence of the Algorithm
- 9.3.Self-Organizing Trees for Binary Classification Problems
- 9.3.1.Construction of the Algorithm
- 9.3.2.Convergence of the Algorithm
- 9.4.Numerical Results
- 9.4.1.Numerical Results for Regression Problems
- 9.4.2.Numerical Results for Classification Problems
- Appendix 9.A
- 9.A.1.Proof of Theorem 1
- 9.A.2.Proof of Theorem 2
- Acknowledgments
- References
