@inproceedings{Deventer02:UNM,
    type = {inproceedings},
    key = {Deventer02:UNM},
    title = {Using Non-Markov models for the control of Dynamic Systems},
    author = {Rainer Deventer and Joachim Denzler and Heinrich Niemann},
    booktitle = {Engineering of Intelligent Systems (EIS)},
    year = {2002},
    month = {September},
    pages = {70 (complete pa},
    publisher = {ICSC-NAISO Academic Press},
    abstract = {Due to shorter life cycles and more complex production processes the automatic generation of models for control purposes is of great importance. Even though Bayesian networks have proven their usefulness in machine learning and pattern recognition and the close relationship between Dynamic Bayesian networks and Kalman Filters respectively difference equations they have not been applied to problems in the area of automatic control. In our work we deduce the structure of a Dynamic Bayesian networks using the state space description and difference equations. Both models are trained by the EM algorithm and used for control purposes. The experiments show that both models performs well, but the training process of the model based on difference equations is much more stable.},
}