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                    基于隨機配置網絡的井下供給風量建模

                    王前進 楊春雨 馬小平 張春富 彭思敏

                    王前進,  楊春雨,  馬小平,  張春富,  彭思敏.  基于隨機配置網絡的井下供給風量建模.  自動化學報,  2021,  47(8): 1963?1975 doi: 10.16383/j.aas.c190602
                    引用本文: 王前進,  楊春雨,  馬小平,  張春富,  彭思敏.  基于隨機配置網絡的井下供給風量建模.  自動化學報,  2021,  47(8): 1963?1975 doi: 10.16383/j.aas.c190602
                    Wang Qian-Jin,  Yang Chun-Yu,  Ma Xiao-Ping,  Zhang Chun-Fu,  Peng Si-Min.  Underground airflow quantity modeling based on SCN.  Acta Automatica Sinica,  2021,  47(8): 1963?1975 doi: 10.16383/j.aas.c190602
                    Citation: Wang Qian-Jin,  Yang Chun-Yu,  Ma Xiao-Ping,  Zhang Chun-Fu,  Peng Si-Min.  Underground airflow quantity modeling based on SCN.  Acta Automatica Sinica,  2021,  47(8): 1963?1975 doi: 10.16383/j.aas.c190602

                    基于隨機配置網絡的井下供給風量建模

                    doi: 10.16383/j.aas.c190602
                    基金項目: 國家自然科學基金(61873272, 61603392), 江蘇省自然科學基金(BK20191043), 江蘇省“雙創團隊” 項目(2017), 鹽城工學院校級科研項目(xjr2019018)資助
                    詳細信息
                      作者簡介:

                      王前進:鹽城工學院電氣工程學院講師. 2018年獲得中國礦業大學博士學位. 主要研究方向為數據驅動建模與控制, 機器學習算法. E-mail: wangqianjinabc@163.com

                      楊春雨:中國礦業大學信息與控制工程學院教授. 2009年獲得東北大學博士學位. 主要研究方向為廣義系統和魯棒控制. E-mail: chunyuyang@cumt.edu.cn

                      馬小平:中國礦業大學信息與控制工程學院教授. 主要研究方向為過程控制, 網絡控制系統, 故障診斷. 本文通信作者. E-mail: xpma@cumt.edu.cn

                      張春富:鹽城工學院電氣工程學院副教授. 2007年獲得哈爾濱工業大學博士學位. 主要研究方向為自動化測控. E-mail: zhangchunfu@hit.edu.cn

                      彭思敏:鹽城工學院副教授, IEEE高級會員. 2013年獲得上海交通大學電力系統及其自動化專業博士學位. 主要研究方向為新能源發電, 電池儲能系統及微電網控制技術. E-mail: psmsteven@163.com

                    Underground Airflow Quantity Modeling Based on SCN

                    Funds: Supported by National Natural Science Foundation of China (61873272, 61603392), Natural Science Foundation of Jiangsu Province (BK20191043), Jiangsu Dual Creative Teams Programme Project (2017), Funding for School-Level Research Projects of Yancheng Institute of Technology (xjr2019018)
                    More Information
                      Author Bio:

                      WANG Qian-Jin Lecturer at the School of Electrical Engineering, Yancheng Institute of Technology. He received his Ph. D. degree from China University of Mining and Technology in 2018. His research interest covers data-driven modeling and control, and machine learning algorithm

                      YANG Chun-Yu Professor at the School of Information and Control Engineering, China University of Mining and Technology. He received his Ph. D. degree from Northeastern University in 2009. His research interest covers descriptor systems and robust control

                      MA Xiao-Ping Professor at the School of Information and Control Engineering, China University of Mining and Technology. His research interest covers process control, networked control system, and fault detection. Corresponding author of this paper

                      ZHANG Chun-Fu Associate professor at the School of Electrical Engineering, Yancheng Institute of Technology. He received his Ph. D. degree from Harbin Institute of Technology in 2007. His research interest covers automatic test and control

                      PENG Si-Min  Associate professor at Yancheng Institute of Technology, IEEE senior member. He received his Ph. D. degree in electric power system and its automation from Shanghai Jiao Tong University in 2013. His research interest covers control of renewable sources generation, battery energy storage system and microgrid

                    • 摘要:

                      主通風機切換過程中, 取壓風量測量作為監測井下供給風量的主要手段, 是礦井主扇通風系統安全、穩定與經濟運行的重要保障. 然而, 由于取壓孔極易出現堵塞現象, 需要頻繁維護, 導致無法實時測量井下供給風量, 難以實現主通風機切換過程的閉環優化控制. 同時, 隨著隱含層節點數的增加, 基于隨機配置網絡(Stochastic configuration network, SCN)的估計模型存在過擬合和泛化能力差的缺點. 為了解決上述問題, 結合正則化(Regularization, R)技術, 本文提出一種新型的改進SCN算法, 即RSC算法, 用于井下供給風量的建模. 基準回歸分析和工業實驗表明: 與SCN方法相比, 建立的RSC模型具有較高的模型精度和較好的泛化性能.

                    • 圖  1  主通風機切換過程示意圖

                      Fig.  1  Diagram of a main fan switchover process

                      圖  2  不同$C$下RSC-II和SCN-III算法對函數近似的訓練與測試精度結果對比

                      Fig.  2  The result comparison of training and testing accuracy of RSC-II and SCN-III on function approximation with varying $C$

                      圖  3  (a)包含5 %異常值的1 000個函數近似訓練樣本和目標函數; (b)兩種算法對測試數據的近似性能

                      Fig.  3  (a) 1 000 training samples containing 5 % outliers for function approximation and target function; (b) Approximation performance on the test dataset by two learning algorithms

                      圖  4  平均兩種算法在測試數據集上的測試RMSE

                      Fig.  4  Average testing RMSE of the two algorithms on the test dataset

                      圖  5  不同$L_{\max}$下RSC-II和SCN-III算法對基準數據集的測試RMSE對比

                      Fig.  5  Test RMSE comparison of the RSC-II and SCN-III algorithms on four benchmark datasets with different $L_{\max}$

                      圖  6  平均兩種算法在實際MFSP數據集上的測試RMSE

                      Fig.  6  Average testing RMSE of the two algorithms on the actual MFSP dataset

                      圖  7  $L_{\max} = 50$所對應的MFSP數據集的測試性能: (a) SCN-III; (b) RSC-II

                      Fig.  7  Test performance at $L_{\max} = 50$ on the actual MFSP dataset: (a) SCN-III; (b) RSC-II

                      表  1  主通風機切換過程相關變量

                      Table  1  Related variables in the MFSP

                      變量符號單位
                      井下供給風量$Q_{\rm 0}$${\rm m^3/s}$
                      井下風阻$R_{\rm 0}$${\rm kg/m^7}$
                      一號垂直風門風量$Q_{\rm 1c}$${\rm m^3/s}$
                      一號垂直風門風阻$R_{\rm 1c}$${ \rm kg/m^7}$
                      一號水平風門風量$Q_{\rm 1s}$${\rm m^3/s}$
                      一號水平風門風阻 $R_{\rm 1s}$${\rm kg/m^7}$
                      一號主通風機風量 $Q_{\rm 1m}$${\rm m^3/s}$
                      一號主通風機壓頭$H_{\rm 1d}$${\rm Pa}$
                      二號垂直風門風量$Q_{\rm 2c}$${\rm m^3/s}$
                      二號垂直風門風阻$R_{\rm 2c}$${\rm kg/m^7}$
                      二號水平風門風量$Q_{\rm 2s}$${\rm m^3/s}$
                      二號水平風門風阻$R_{\rm 2s}$${\rm kg/m^7}$
                      二號主通風機風量$Q_{\rm 2m}$${\rm m^3/s}$
                      二號主通風機壓頭$H_{\rm 2d}$${\rm Pa}$
                      下載: 導出CSV

                      表  2  函數近似的性能比較

                      Table  2  Performance comparisons on the function approximation

                      算法不同$L_{\max}$所對應的測試性能 (Mean, STD)
                      507090110130150170190
                      SCN-III0.0315, 0.00320.0329, 0.00330.0388, 0.00480.0396, 0.00320.0459, 0.00430.0512, 0.00580.0526, 0.00480.0557, 0.0095
                      RSC-II0.0229, 0.00190.0209, 0.00130.0209, 0.00080.0209, 0.00030.0210, 0.00050.0211, 0.00040.0213, 0.00040.0218, 0.0003
                      下載: 導出CSV

                      表  3  回歸數據集與參數設置

                      Table  3  Specifications of benchmark problems and some parameter settings

                      數據集屬性輸出訓練數據測試數據${\lambda_{\min}}$${\Delta\lambda}$
                      Wine Quality121391898010.1
                      Concrete9177225810.1
                      Yacht712278111
                      Airfoil Self-noise61135215110.1
                      下載: 導出CSV

                      表  4  對基準數據集的性能對比

                      Table  4  Performance comparisons on benchmark datasets

                      數據集算法不同$L_{\max}$所對應的測試性能 (Mean, STD)
                      507090110130150170190
                      (a)SCN-III0.2476, 0.02330.2546, 0.05310.2543, 0.04920.2559, 0.04640.2908, 0.06120.2997, 0.08410.3480, 0.14840.3934, 0.1886
                      RSC-II0.2232, 0.00110.2229, 0.00140.2229, 0.00160.2225, 0.00160.2231, 0.00170.2235, 0.00220.2236, 0.00210.2238, 0.0021
                      (b)SCN-III0.4017, 0.10600.5209, 0.15870.7120, 0.22470.7739, 0.20100.8640, 0.23241.3580, 0.79451.4025, 0.45261.6700, 0.5415
                      RSC-II0.2248, 0.00680.2277, 0.00590.2295, 0.00940.2295, 0.00760.2323, 0.00890.2341, 0.00900.2384, 0.00630.2416, 0.0056
                      (c)SCN-III0.3167, 0.22890.3528, 0.14360.3820, 0.17810.6620, 0.29000.9062, 0.56001.2226, 0.35712.7450, 1.17184.0242, 1.0377
                      RSC-II0.1308, 0.02090.1216, 0.01070.1197, 0.01520.1168, 0.01110.1164, 0.01180.1113, 0.01170.1056, 0.01120.1066, 0.0113
                      (d)SCN-III0.2607, 0.03130.3489, 0.10070.4332, 0.12190.6615, 0.24871.2668, 0.42131.4295, 0.58831.8264, 0.51942.0539, 0.9744
                      RSC-II0.2281, 0.00910.2378, 0.01470.2332, 0.01530.2395, 0.02140.2443, 0.02790.2615, 0.04020.2437, 0.02340.2848, 0.0512
                      下載: 導出CSV

                      表  5  在實際MFSP數據集上的性能對比

                      Table  5  Performance comparisons on the actual MFSP dataset

                      算法不同$L_{\max}$所對應的測試性能 (Mean, STD)
                      50110170230270330370410
                      SCN-III0.0287, 0.00290.0494, 0.00390.0623, 0.00690.0758, 0.00630.0837, 0.00590.0983, 0.01070.1033, 0.00960.1146, 0.0095
                      RSC-II0.0269, 0.00210.0316, 0.00170.0372, 0.00350.0411, 0.00460.0424, 0.00470.0454, 0.00660.0460, 0.00540.0509, 0.0077
                      下載: 導出CSV
                      360彩票
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                    • 收稿日期:  2019-08-24
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