Abstract
Accurate prediction of Tongguan Elevation has important realistic significance in flood control of lower Wei River. This paper builds a prediction model of Tongguan Elevation changes in Non-flood seasons combined with T-S fuzzy reasoning and association rule learning, based on historical data of Tongguan of the Yellow River. Considering the “curse of dimensionality” during fuzzy reasoning, the model uses association analysis learning to prune the amount of fuzzy rules, ensuring the simplicity and effectiveness of the final reasoning rules. The results of prediction based on historical data indicate that this model has high efficiency and accuracy; the accuracy is better than the existing BP prediction model.
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References
Zhou, J., Lin, B.: Awareness of Tongguan Elevation in the Yellow River. China Water (6), 47–49 (2003)
Li, W., Wu, B.: Analysis of Tongguan Elevation and factors. Yellow River 32(7) (2010)
Chen, J., Liu, Y., Hu, M., Zhang, J.: Prediction of Tongguan Elevation based on improved BP Neural Networks. Journal of Hydraulic Engineering (8), 96–100 (2003)
Zhang, J., Wang, Y.: Analysis of Tongguan Elevation in non-flood seasons and operation mode of Sanmenxia Reservoirr. Water Resources and Hydropower Engineering 6, 54–58 (2002)
Guo, F.: Development of fuzzy reasoning. Shanxi RTVU Journal (4), 71–74 (2007)
Jia, W., Jia, W.: Introduction of researches and applications on fuzzy reasoning. Science & Technology Information (6) (2008)
Taksgi, T., Sugeno, M.: Fuzzy indentification of systems and its application to modeling and control. IEEE Trans. on SMC 15(1), 36–39 (1985)
Zhong, Q., Yu, Y., Xu, S.: Impulsive control for T-S fuzzy model based Chaotic Systems with Adaptive Feedback. In: International Conference on Communications, Circuits and Systems, ICCCAS 2009, pp. 872–875 (2009)
Zhang, Z., Zhang, P.: Neural Fuzzy and Soft Computing. Xi’an Jiaotong University Press (2000)
Yellow River Water Resources Research Institute, The effect of operation of Sanmenxia Reservoirr to Tongguan Elevation, Yellow River Water Resources Research Institute (March 2004)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487–499 (September 1994)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Du, X., Du, X., Luo, X., Yang, F. (2012). Prediction Model of Tongguan Elevation Changes in Non-flood Seasons Based on T-S Fuzzy Reasoning. In: Jin, D., Lin, S. (eds) Advances in Future Computer and Control Systems. Advances in Intelligent and Soft Computing, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29387-0_40
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DOI: https://doi.org/10.1007/978-3-642-29387-0_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29386-3
Online ISBN: 978-3-642-29387-0
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