Title :
Automated Sample Data Selecting from DAS Based on Maximum Entropy Theory
Author :
Wang Yue-long ; Huo Ai-qing ; Xu De-min
Author_Institution :
Sch. of Marine Eng., Northwestern Polytech. Univ., Xi´an, China
Abstract :
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.
Keywords :
data acquisition; entropy; learning (artificial intelligence); radial basis function networks; sampling methods; DAS; RBF-BP cascaded artificial neural network; automated sample data selecting; boundary data points; continuous system; equipartitional method; ethylene oxide reactor modeling; machine learning; maximum entropy theory; sample data selection; Artificial neural networks; Automatic control; Continuous time systems; Entropy; Information theory; Laboratories; Learning systems; Machine learning; Sampling methods; Testing;
Conference_Titel :
Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5872-1
Electronic_ISBN :
978-1-4244-5874-5
DOI :
10.1109/IWISA.2010.5473673
