• Laser & Optoelectronics Progress
  • Vol. 59, Issue 18, 1815018 (2022)
Fubin Wang1, Rui Wang1、*, and Chen Wu2
Author Affiliations
  • 1College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei , China
  • 2Tang Steel International Engineering Technology Co., Ltd., Tangshan 063000, Hebei , China
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    DOI: 10.3788/LOP202259.1815018 Cite this Article Set citation alerts
    Fubin Wang, Rui Wang, Chen Wu. Short-Term Prediction of Sintering State Based on Improved Random Forest[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815018 Copy Citation Text show less
    References

    [1] Patisson F, Bellot J P, Ablitzer D. Study of moisture transfer during the strand sintering process[J]. Metallurgical Transactions B, 21, 37-47(1990).

    [2] Li L. On line controlling of burning through point based on numerical simulation[D](2011).

    [3] Wu X F. Research on prediction and control of burning through point based on support vector machines in the sintering process[D](2009).

    [4] Cheng P Z. Research on diversified recommendation algorithm based on supervised learning[D](2017).

    [5] Zhang Z W, Bo F H, Zhou F et al. Self adaptive modeling of burn though point in sintering process based on time series[J]. World Nonferrous Metals, 264-266(2018).

    [6] Zhou J P. Research on forecasting and fuzzy control of sintering endpoint based on volume model[J]. Journal of Anhui University of Technology (Natural Science), 36, 62-67(2019).

    [7] Wang S H, Li H F, Zhang Y J et al. Prediction and analysis of burning though point base on modified AdaBoost.RS algorithm[J]. China Metallurgy, 29, 13-19(2019).

    [8] Rahman R, Otridge J, Pal R. IntegratedMRF: random forest-based framework for integrating prediction from different data types[J]. Bioinformatics, 33, 1407-1410(2017).

    [9] Niu Z H, Qu J Y, Wu R B. Random forest algorithm using stratified subspaces and weighted trees based on Spark[J]. Journal of Signal Processing, 33, 1301-1307(2017).

    [10] Liu J. Constrained random forest algorithm for single image super-resolution[J]. Computer Engineering and Design, 38, 970-975(2017).

    [11] Wu C W, Liang J H, Wang W et al. Random forest algorithm for sequential response: prediction and selection of variables[J]. Journal of Chinese Computer Systems, 38, 1762-1766(2017).

    [12] Xia J, Zhang S Y, Cai G L et al. Adjusted weight voting algorithm for random forests in handling missing values[J]. Pattern Recognition, 69, 52-60(2017).

    [13] Shi J Y, Yang Z Y, Xie X. Algorithm of random forest based on fuzzy decision[J]. Computer Engineering and Design, 41, 2207-2212(2020).

    [14] Wang Q, Zeng W D, Xia Z P et al. Recognition of food-borne pathogenic bacteria by Raman spectroscopy based on random forest algorithm[J]. Chinese Journal of Lasers, 48, 0311002(2021).

    [15] Wang M, Zhang X C, Wang J Y et al. Forest resource classification based on random forest and object oriented method[J]. Acta Geodaetica et Cartographica Sinica, 49, 235-244(2020).

    [16] Adnan M N, Islam M Z. Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm[J]. Knowledge-Based Systems, 110, 86-97(2016).

    [17] Drakakis G, Moledina S, Chomenidis C et al. Decision trees for continuous data and conditional mutual information as a criterion for splitting instances[J]. Combinatorial Chemistry & High Throughput Screening, 19, 423-428(2016).

    [18] Zhao W L, Deng C H, Ngo C W. K-means: a revisit[J]. Neurocomputing, 291, 195-206(2018).

    [19] Xu Y G. Pattern recognition of traffic flows in elevator group control systems based on SVM[J]. Journal of South China University of Technology, 33, 32-35(2005).

    Fubin Wang, Rui Wang, Chen Wu. Short-Term Prediction of Sintering State Based on Improved Random Forest[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815018
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