2006
NeurIPS
NeurIPS 2006
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
Abstract
In this paper, we present a subspace method for learning nonlinear dynamical systems based on stochastic realization, in which state vectors are chosen using kernel canonical correlation analysis, and then state-space systems are identified through regression with the state vectors. We construct the theoretical underpinning and derive a concrete algorithm for nonlinear identification. The obtained algorithm needs no iterative optimization procedure and can be implemented on the basis of fast and reliable numerical schemes. The simulation result shows that our algorithm can express dynamics with a high degree of accuracy.
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Conference Pioneer
- NeurIPS 2006
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics and Mathematics & Optimization
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Trend Setter
- Stochastic Processes
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Keyword Pioneer
- subspace learning
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Hot Topic Early Bird
- state space model
Authors
Topics
Machine Learning > Core Methods > Regression
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Theory
Data Science & Analytics > Methods > Time Series
Data Science & Analytics > Methods > Time Series Analysis
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Kernel Methods
Mathematics & Optimization > Probability > Stochastic Processes
Mathematics & Optimization > Optimization > Kernel Methods
Keywords
subspace learning
stochastic realization
kernel canonical correlation
subspace identification
canonical correlation analysis
state space model
dynamical system
nonlinear dynamical system
subspace method
kernel methods
kernel canonical correlation analysis
state-space system
state-space identification