System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A greedy approach to identification of piecewise affine models
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Comparison of four procedures for the identification of hybrid systems
HSCC'05 Proceedings of the 8th international conference on Hybrid Systems: computation and control
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
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This paper adapts the Gibbs sampling method to the problem of hybrid system identification. We define a Generalized Linear Hiddenl Markov Model (GLHMM) that combines switching dynamics from Hidden Markov Models, with a Generalized Linear Model (GLM) to govern the continuous dynamics. This class of models, which includes conventional ARX models as a special case, is particularly well suited to this identification approach. Our use of GLMs is also driven by potential applications of this approach to the field of neural prosthetics, where neural Poisson-GLMs can model neural firing behavior. The paper gives a concrete algorithm for identification, and an example motivated by neuroprosthetic considerations.