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Advisor(s)
Abstract(s)
In some control systems structures, like predictive control, mathematical models
for the control process must be derived. Those models can be obtained by a broad class
of methods like parametric models applied to experimental data. In this context, and for
systems with multiple operation regimes, the Hidden Markov model, due to its properties,
is a convincing choice. However the parameter estimation of this type of models involves
the optimization of a non-convex cost function. So the Baum-Welch method only can find
sub-optimal parameters. This article shows that the use of the Taguchi method minimizes the
training algorithm sensibility local minima.
Description
Keywords
Hidden Markov models Taguchi method Optimization problems
Citation
Coelho, João; Cunha, José; Oliveira, Paulo (2010). Training hidden markov models with the taguchi method. In 9th Portuguese Conference on Automatic Control.