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HYBRID
2004
Springer

Inference Methods for Autonomous Stochastic Linear Hybrid Systems

12 years 7 months ago
Inference Methods for Autonomous Stochastic Linear Hybrid Systems
We present a parameter inference algorithm for autonomous stochastic linear hybrid systems, which computes a maximum-likelihood model, given only a set of continuous output data of the system. We overcome the potentially intractable problem of identifying the sequence of discrete modes by using dynamic programming; we compute the maximumlikelihood continuous models using an Expectation-Maximization technique. This allows us to find a maximum-likelihood model in time that is polynomial in the number of discrete modes as well as in the length of the data series. We prove local convergence of the algorithm. We also propose a novel initialization technique to derive good initial conditions for the model parameters. Finally, we demonstrate our algorithm on some examples - two simple one-dimensional examples with simulated data, and an application to real flight test data from a dual-vehicle demonstration of the Stanford DragonFly Unmanned Aerial Vehicles.
Hamsa Balakrishnan, Inseok Hwang, Jung Soon Jang,
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where HYBRID
Authors Hamsa Balakrishnan, Inseok Hwang, Jung Soon Jang, Claire Tomlin
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