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One-Step Ahead Prediction of the Wiener-Hammerstein Benchmark with Process Noise using Kernel Adaptive Learning
Rishi Relan Dieter Verbecke Koen Tiels Department ELEC 12.05.2017
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One-Step Ahead Prediction of the Wiener-Hammerstein Benchmark with - - PowerPoint PPT Presentation
One-Step Ahead Prediction of the Wiener-Hammerstein Benchmark with - - PowerPoint PPT Presentation
One-Step Ahead Prediction of the Wiener-Hammerstein Benchmark with Process Noise using Kernel Adaptive Learning Rishi Relan Dieter Verbecke Koen Tiels Department ELEC 12.05.2017 Idea of this workshop: Nonlinear System 2 Idea of this
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Idea of this workshop: Different methods
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Overview
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Benchmark
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Physical Electronic Circuit
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Linear Adaptive Filters
- Widrow and Hoff (1960): LMS filter
- Kalman (1960): Kalman filter
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Two Simple Adaptive Filters
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Nonlinear Adaptive Filter
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Kernel Methods
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Putting it formally
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Kernel Regression
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The Standard Online Algorithm
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The main Questions?
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The Bayesian Viewpoint
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Further Extensions
1) Steven Van Vaerenbergh, Miguel Lázaro-Gredilla and Ignacio Santamaría, "Kernel Recursive Least-Squares Tracker for Time-Varying Regression," IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 8, pp. 1313-1326, Aug 2012. 2) F. Perez-Cruz, S. Van Vaerenbergh, J. J. Murillo-Fuentes, M. Lazaro-Gredilla and I. Santamaria, "Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances," in IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 40-50, July 2013.
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Observations
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Observations
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Conclusions
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