Pub. online:1 Jan 2007Type:Research ArticleOpen Access
Volume 18, Issue 2 (2007), pp. 163–186
In this article we present the general architecture of a hybrid neuro-symbolic system for the selection and stepwise elimination of predictor variables and non-relevant individuals for the construction of a model. Our purpose is to design tools for extracting the relevant variables and the relevant individuals for an automatic training from data. The objective is to reduce the complexity of storage, therefore the complexity of calculation, and to gradually improve the performance of ordering, that is to say to arrive at a good quality training.
Pub. online:1 Jan 2005Type:Research ArticleOpen Access
Volume 16, Issue 4 (2005), pp. 473–502
To offer high quality services, when users are increasingly demanding and competition more and more hard, is now a major problem that transportation companies are faced with. So, ensuring a regular traffic needs to identify the randomly occurring disturbances that affect the transportation system and to eliminate or reduce their impacts on the traffic.
This paper presents a decision support system TRSS (Traffic Regulation Support System). TRSS is a supervision environment for the regulation of urban transportation system. TRSS (tram and bus) is based on the regulation operator decision-making process. It provides the operator with the information he needs to identify disturbances and evaluate potential corrective actions to be carried out, according to the regulation strategy he has selected.
The first part of the paper presents the decision model we work with. The second part deals with the functional model used in the decision support system. Decision support system for transportation and characteristics of a DSS for a transportation system are described in the third part. In the fourth part, we present the components of the decision-making TRSS supervision tool. In the fifth part, we present the criteria of evaluation and the sixth part is devoted to the presentation of the results.