A carregar...
Publicação
Training hidden markov models with the taguchi method
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 152 KB | Adobe PDF |
Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Hidden Markov models Taguchi method Optimization problems
Contexto Educativo
Citação
Coelho, João; Cunha, José; Oliveira, Paulo (2010). Training hidden markov models with the taguchi method. In 9th Portuguese Conference on Automatic Control.