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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.
Neural Networks, 2003. Proceedings of the International Joint Conference on (Volume:1 )
Date of Conference: 20-24 July 2003