山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (5): 20-28.doi: 10.6040/j.issn.1672-3961.0.2022.162
• 机器学习与数据挖掘 • 上一篇
韦修喜1,陶道2,黄华娟1*
WEI Xiuxi1, TAO Dao2, HUANG Huajuan1*
摘要： 针对BP神经网络存在预测精度不佳、预测结果不稳定的问题,提出改进果蝇算法优化BP神经网络(back propagation neural network)预测模型。将混沌映射、判别因子与变步长机制引入果蝇优化算法(fruit fly optimization algorithm, FOA)中,得到改进后的自适应混沌果蝇优化算法(fruit fly optimization algorithm with chaos and discriminant factors, CDFOA),并利用测试函数对算法进行性能验证。利用CDFOA优化BP神经网络的初始权值与阈值,构建基于CDFOA优化BP神经网络对于汽油辛烷值的预测模型CDFOA-BP。将采集到的60组汽油数据输入预测模型进行测试分析。预测结果表明,与FOA-BP模型、PSO-BP模型、SSA-BP模型和BP神经网络模型相比,CDFOA-BP模型在预测精度与预测稳定性上均优于其他4种模型,验证该模型的有效性与可行性。
中图分类号:
| [1] TIAGO D, RODOLFO O, PEDRO S, et al. Predictive analytics in the petrochemical industry: research octane number(RON)forecasting and analysis in an industrial catalytic reforming unit[J]. Computers and Chemical Engineering, 2020, 139: 106912. [2] 丁怡曼,薛晓康,范宾,等. 基于PLS-红外光谱的汽油辛烷值测定方法研究[J]. 化学研究与应用, 2021, 33(5): 863-867. DING Yiman, XUE Xiaokang, FAN Bin, et al. Determination of gasoline octane number based on PLS-Infrared spectroscopy[J]. Chemical Research and Application, 2021, 33(5): 863-867. [3] 褚小立,陈瀑,李敬岩,等. 近红外光谱分析技术的最新进展与展望[J]. 分析测试学报, 2020, 39(10): 1181-1188. CHU Xiaoli, CHEN Pu, LI Jingyan, et al. Progresses and perspectives of near infrared spectroscopy analytical technology[J]. Journal of Instrumental Analysis, 2020, 39(10): 1181-1188. [4] 夏良. 低压环境汽油辛烷值测定技术研究[J]. 仪器仪表用户, 2021, 28(9): 6-9. XIA Liang. Study on determination of octane number of gasoline in low pressure environment[J]. Instrumen-tation, 2021, 28(9):6-9. [5] 赵林,李希,谢永芳,等. 基于自适应变量加权的汽油辛烷值预测方法[J].控制与决策, 2022, 37(10):2738-2744. ZHAO Lin, LI Xi, XIE Yongfang, et al. Prediction method of gasoline octane number based on adaptive variable weighting[J]. Control and Decision, 2022, 37(10):2738-2744. [6] 郑斌,孙洪霞,王维民. 基于随机森林回归的汽油研究法辛烷值预测[J]. 石油炼制与化工, 2020, 51(12):69-75. ZHENG bin, SUN Hongxia, WANG Weimin. Prediction of gasoline research octane number based on random forest regression[J]. Petroleum Procession and Petrochemical, 2020, 51(12): 69-75. [7] 杨丰瑞,罗思烦,李前洋. 基于特征选择的极限随机森林算法研究[J]. 计算机应用研究, 2020, 37(9):2625-2628. YANG Fengrui, LUO Sifan, LI Qianyang. Research on extreme random forest algorithm based on feature selection[J]. Application Research of Computers, 2020, 37(9):2625-2628. [8] PAN W T. A new fruit fly optimization algorithm: taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012, 26(2): 69-74. [9] 凤丽洲,王友卫,韩琳琳,等. 双重驱动的果蝇优化算法及其在PID控制器中的应用[J]. 控制与决策, 2021, 36(9):2225-2233. FENG Lizhou, WANG Youwei, HAN Linlin, et al. Double drive fruit fly optimization algorithm and its application in PID controller[J]. Control and Decision, 2021, 36(9):2225-2233. [10] 黄小莉,陈静娴,胡思宇. 基于自适应果蝇优化算法的K-means聚类[J]. 国外电子测量技术, 2021, 40(6):14-20. HUANG Xiaoli, CHEN Jingxian, HU Siyu. K-means clustering based on adaptive fruit fly optimization algorithm[J]. Foreign Electronic Measurement Technology, 2021, 40(6):14-20. [11] 谭晶晶. 基于动态变步长果蝇算法优化支持向量机的轴承故障诊断方法[J]. 机械设计与研究, 2021, 37(1):102-105. TAN Jingjing. A fault diagnosis method based on SVM optimized by improved fruit fly optimization algorithm[J]. Machine Design and Research, 2021, 37(1):102-105. [12] 石建平,刘国平,李培生,等. 双策略协同进化果蝇优化算法及其应用[J].计算机集成制造系统, 2022, 28(5):1482-1495. SHI Jianping, LIU Guoping, LI Peisheng, et al. Double strategies co-evolutionary fruit fly optimization algorithm and its application[J]. Computer Integrated Manufac-turing Systems, 2022, 28(5):1482-1495. [13] 曾宇容,王林,王思睿.基于混合果蝇优化算法的选址-库存-配送集成优化研究[J].运筹与管理, 2022, 31(3):24-30. ZENG Yurong, WANG Lin, WANG Sirui. Integrated optimization of location-inventory-delivery problem using hybrid fruit fly optimization algorithm[J]. Operations Research and Management Science, 2022, 31(3):24-30. [14] FAN Yibo, JIN Yize, MENG Zihao, et al. Pixels and panoramas: an enhanced cubic mapping scheme for video/image-based virtual-reality scenes[J]. IEEE Consumer Electronics Magazine, 2019, 8(2):44-49. [15] 吴远超,舒乃秋,关向雨. 基于LSSVM和混沌理论改进人工蜂群优化算法的GIS母线接头温度预测[J]. 电测与仪表, 2018, 55(20): 8-14. WU Yuanchao, SHU Naiqiu, GUAN Xiangyu. Temperature prediction method of GIS bus connector based on improved artificial bee colony optimization LSSVM algorithm[J]. Electrical Measurement & Instrumentation, 2018, 55(20): 8-14. [16] 张孟健,张浩,陈曦,等. 基于Cubic映射的灰狼优化算法及应用[J]. 计算机工程与科学, 2021, 43(11): 2035-2042. ZHANG Mengjian, ZHANG Hao, CHEN Xi, et al. Grey wolf optimization algorithm based on Cubic mapping and its application[J]. Computer Engineering & Science, 2021, 43(11): 2035-2042. [17] HUANG Dongmei, HE Shiqing, HE Xuhui, et al. Prediction of wind loads on high-rise building using a BP neural network combined with POD[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2017, 170:1-17. [18] ALJARAH I, FARIS H, MIRJALILI S. Optimizing connection weights in neural networks using the whale optimization algorithm[J]. Soft Computing, 2018, 22(1): 1-15. [19] 刘畅,刘天琪, 陈振寰,等. 基于KPCA和BP神经网络的短期负荷预测[J]. 电测与仪表, 2016, 53(10): 57-61. LIU Chang, LIU Tianqi, CHEN Zhenhuan, et al. Short-term power load forecasting based on kernel principal component analysis and BP neural network[J]. Electrical Measurement & Instrumentation, 2016, 53(10): 57-61. [20] 王克杰,张瑞. 基于改进 BP 神经网络的短期电力负荷预测方法研究[J]. 电测与仪表, 2019, 56(24): 115-121. WANG Kejie, ZHANG Rui. Research on short-term power load forecasting method based on improved BP neural network[J]. Electrical Measurement & Instrumentation, 2019, 56(24): 115-121. |
| [1] | 李浩源,于景明,张桂林,张斌. 基于智能算法的光纤预制棒芯层制备工艺参数优化[J]. 山东大学学报 (工学版), 2023, 53(4): 149-156. |
| [2] | 丁飞,江铭炎. 基于改进狮群算法和BP神经网络模型的房价预测[J]. 山东大学学报 (工学版), 2021, 51(4): 8-16. |
| [3] | 孙东磊,王艳,于一潇,韩学山,杨明,闫芳晴. 基于BP神经网络的短期光伏集群功率区间预测[J]. 山东大学学报 (工学版), 2020, 50(5): 70-76. |
| [4] | 金保明,卢光毅,王伟,杜伦阅. 基于弹性梯度下降算法的BP神经网络降雨径流预报模型[J]. 山东大学学报 (工学版), 2020, 50(3): 117-124. |
| [5] | 方波,陈红梅. 一种新的双策略进化果蝇优化算法[J]. 山东大学学报 (工学版), 2019, 49(3): 22-31. |
| [6] | 郑店坤,许同乐,尹召杰,孟庆民. 改进PSO-BP神经网络对尾矿坝地下水位的预测方法[J]. 山东大学学报 (工学版), 2019, 49(3): 108-113. |
| [7] | 杨亚楠,夏斌,谢楠,袁文浩. 基于BP神经网络和多元Taylor级数的混合定位算法[J]. 山东大学学报 (工学版), 2019, 49(1): 36-40. |
| [8] | 刘杰, 杨鹏, 吕文生, 刘阿古达木, 刘俊秀. 基于气象因素的PM2.5质量浓度预测模型[J]. 山东大学学报(工学版), 2015, 45(6): 76-83. |
| [9] | 姚福安,庞向坤,焦营营,王忠林,张锡满 . 基于三色法和BP神经网络的回转窑温度检测[J]. 山东大学学报(工学版), 2008, 38(2): 61-65 . |
| [10] | 高小伟,蒋晓芸 . BP神经网络在入侵检测系统中的应用及优化[J]. 山东大学学报(工学版), 2006, 36(6): 107-110 . |
|
||
