Training hidden markov models with the taguchi method - Coelho, J.P. (2011)
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.