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Identification of dynamical systems using GMM with VQ initialization

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3 Author(s)
Jing Lan ; Comput. NeuroEngineering Lab., Florida Univ., Gainesville, FL, USA ; Principe, J.C. ; Motter, M.A.

We are using Gaussian mixture models (GMM) as a tool to construct local mappings of nonlinear multi-input multi-output (MIMO) systems. In this work, we combine the advantages of GMM with the Kalman filter. To improve the accuracy of the local linear mappings in a potentially large dimensional state space, we propose to initialize the GMM parameters with vector quantization (VQ) or its more parsimonious counterpart growing self-organizing maps (G-SOM). The performance of the proposed modeling algorithm on simulated data obtained from a realistic aircraft model show improvements in both converge speed and accuracy.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:1 )

Date of Conference:

20-24 July 2003