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Erschienen in:
2013 | OriginalPaper | Buchkapitel
An Online Trend Analysis Method for Measuring Data Based on Historical Data Clustering
verfasst von : Jianfeng Qu, Maoyun Guo, Yi Chai, Zhimin Yang, Tao Zou, Tian Lan, Zhenglei Liu
Erschienen in: Advances in Swarm Intelligence
Verlag: Springer Berlin Heidelberg
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It is important to analyze and predict the measuring data trend in industrial measuring and controlling process. The paper introduces a method for predicting the trend of the current measuring data based on clustering the historical data. It calculates the similarities of the current trend and the bases result from the clustering. And with these similarities, the future trend of the current measuring data can be predicted , the combination of the above bases representing low frequency and a reviser representing high frequency. The simulation shows the weights of high or low frequency have effect on the precision of predict results. It is also found that the proposed method can predict more precisely than the RBFNNs method in high frequency.