PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Nonlinear State-Space Models for Control
Tapani Raiko and Matti Tornio
In: International Joint Conference on Neural Networks, IJCNN 2005, 31 Jul - 03 Aug 2005, Montreal, Canada.


This paper studies the learning of nonlinear state-space models for a control task. This has some advantages over traditional methods. Variational Bayesian learning provides a framework where uncertainty is explicitly taken into account and system identification can be combined with model-predictive control. Three different control schemes are used. One of them, optimistic inference control, is a novel method based directly on the probabilistic modelling. Simulations with a cart-pole swing-up task confirm that the latent state space provides a representation that is easier to predict and control than the original observation space.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1526
Deposited By:Tapani Raiko
Deposited On:28 November 2005