Details

Autor(en) / Beteiligte

• Wang, Yue-long
• Huo, Ai-qing
• Xu, De-min

Titel

Automated Sample Data Selecting from DAS Based on Maximum Entropy Theory

Ist Teil von

• 2010 2nd International Workshop on Intelligent Systems and Applications, 2010, p.1-4

Ort / Verlag

IEEE

Erscheinungsjahr

2010

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• How to selecting sample data set from a DAS automatically is a critical problem for machine learning. In this paper, it is illustrated that measurement data included enough information for modeling through comparing the information extropy of a continuous system with its sampling system. Based on maximum entropy principle, an equipartitional method has been discussed which can be used to collect a small data set as a training sample set from a DAS. Then, an application of this method which be used in an ethylene oxide reactor's modeling has been given. The sample set obtained by this way has a uniform distribution as good as distributing of boundary data points. This application illustrates that this way was effectively for selecting a sample set. And combined with a RBF-BP cascaded artificial neural network, it got a satisfactory prediction result.