Prediction of the tendency of grate pressure based on hidden Markov model which is optimized by the improved multiple population genetic algorithm

被引：1
|
作者：
Liu Z.-L. [1 ,2 ]
Zhang C.-L. [1 ,2 ]
Guo C.-J. [1 ,2 ,3 ]
Wang H.-Y. [1 ,2 ]
Wu Y. [1 ,2 ]
Liu B. [1 ,2 ]
机构：
[1] Information Science and Engineering College, Yanshan University, Hebei Province Key Laboratory of Special Optical Fiber and Optical Fiber Sensing, Qinhuangdao, 066004, Hebei
[2] Information Science and Engineering College, Yanshan University, Qinhuangdao, 066004, Hebei
[3] LiRen College, Yanshan University, Qinhuangdao, 066004, Hebei
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Genetic algorithm; Grate; Hidden Markov model; Improved multiple population genetic algorithm; Prediction; Principal component analysis; The grate pressure;
D O I：
10.7641/CTA.2018.80490
中图分类号：
学科分类号：
摘要：
A model for predicting the variation tendency of grate pressure is proposed in this paper, taking the grate pressure as the research object which is the key parameters of grate cooler. Principal component analysis is used to reduce the dimension of data. The principal component feature sequence is used as the observation sequence. An algorithm combined with the improved multiple population genetic algorithm and hidden Markov model is constructed. Individuals are selected by roulette selection operator to avoid local convergence, the adaptive crossover operator is designed, which is combined with double zone crossover and uniform line crossover. The polynomial mutation operation of dynamic mutation rate is applied to improve the convergence speed within population. An immigration operator is presented which is mixed by communication mechanism between teachers and students to ensure the co-evolution of multiple populations. The research results show that the improved algorithm can converge to the global optimum and improve convergence accuracy and speed. The model established by this algorithm exhibits good tracking performance and high prediction accuracy, which is suitable for predicting the variation tendency of grate pressure. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1217 / 1226
页数：9
相关论文
共 19 条
  • [1] Ahamed J.U., Madlool N.A., Saidur R., Et al., Assessment of energy and exergy efficiencies of a grate clinker cooling system through the optimization of its operational parameters, Energy, 46, 1, pp. 664-674, (2012)
  • [2] Kamble L.V., Pangavhane D.R., Singh T.P., Neural network optimization by comparing the performances of the training functions-prediction of heat transfer from horizontal tube immersed in gas-solid fluidized bed, International Journal of Heat and Mass Transfer, 83, pp. 337-344, (2015)
  • [3] Wen Y., Wang J., Yue H., Et al., Equivalent straight pipe heat transfer model of cement clinker porous media in grate cooler, Journal of Chemical Industry and Engineering, 65, 9, pp. 3434-3440, (2014)
  • [4] Pani A.K., Vadlamudi V.K., Mohanta H.K., Development and comparison of neural network based soft sensors for online estimation of cement clinker quality, ISA Transactions, 52, 1, pp. 19-29, (2013)
  • [5] Li S., Liu Z., Kang X., The application of bp neural predictive fuzzy control in cement grate cooler system, Mechatronic Sciences, Electric Engineering and Computer, 8, pp. 181-184, (2013)
  • [6] Li H., Hao X., Liu B., Et al., Modeling for clinker flow of the grate cooler based on fuzzy neural networks, Chinese Journal of Scientific Instrument, pp. 171-173, (2005)
  • [7] Yuan J.L., Zhong L., Du H.F., Et al., Prediction of free lime content in cement clinker based on RBF neural network, Journal of Wuhan University of Technology-Mater, 27, 1, pp. 187-190, (2012)
  • [8] Zhang D., Ning X., Liu X., On-line prediction of nonlinear time series using RBF neural networks, Control Theory & Applications, 26, 2, pp. 151-155, (2009)
  • [9] Zeng C., Yao X., Tang D., Et al., Fault prediction of wind turbing gearbox based on improved hidden Markov model, Acta Energiae Solaris Sinica, 37, 4, pp. 1017-1023, (2016)
  • [10] Purushothham V., Narayanan S., Suryanarayana A.N.P., Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault rection, NET & E International, 38, 8, pp. 654-664, (2005)
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