计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 327-333.doi: 10.11896/jsjkx.191200126
张德干1,2, 杨鹏1,2, 张捷3, 高瑾馨1,2, 张婷1,2
ZHANG De-gan1,2, YANG Peng1,2, ZHANG Jie3, GAO Jin-xin1,2, ZHANG Ting1,2
摘要： 文中提出一种基于量子粒子群优化策略的车联网交通流量预测算法。根据交通流量数据特征建立对应模型,将遗传模拟退火算法应用到量子粒子群算法中得到优化的初始聚类中心,并将优化后的算法应用于径向基神经网络预测模型的参数优化,通过径向基神经网络的高维映射得到所需预测的数据结果。另外,将所提算法与QPSO-RBF等其他相关算法进行了比较研究。仿真结果显示,相比于其他算法,所提算法能够降低预测误差,得到更好、更稳定的预测结果。
中图分类号:
| [1] MEI D,YAN Q,GAO L.A Short-Term Traffic Flow Prediction Model Based on EMD and GPSO-SVM[C]//2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017).2017:2554-2558. [2] MASSANA J,CARLES P,LLORENC B M,et al.Identifying services for short-term load forecasting using data driven models in a Smart City platform[J].Sustainable Cities and Society,2017,28(1):108-117. [3] KOESDWIADY A,BEDAWI S,OU C J,et al.Non-StationaryTraffic Flow Prediction Using Deep Learning[C]//2018 IEEE 88th Vehicular Technology Conference(VTC-Fall).2018:1-5. [4] LIU A Q,LI C L,YUE W W,et al.Real-Time Traffic Prediction:A Novel Imputation Optimization Algorithm with Missing Data[C]//2018 IEEE Global Communications Conference (GLOBE COM).2018:1-7. [5] PETROS I,ZHANG Y H.Intelligent driver assist system forurban driving[J].Digital Media Industry & Academic Forum (DMIAF),2016,1(1):128-134. [6] HU X,WEI X,GAO Y,et al.An Attention-Mechanism-Based Traffic Flow Prediction Scheme for Smart City[C]//2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).2019:1822-1827. [7] OMKAR G,VASANTHA K S.Time series decomposition modelfor traffic flow forecasting in urban midblock sections[C]//2017 International Conference on Smart Technologies For Smart Nation (SmartTechCon).2017:720-723. [8] HAMZAH A N,IMAD M,IMRAN M.Highway Cluster Density and Average Speed Prediction in Vehicular Ad Hoc Networks (VANETs)[C]//2018 IEEE Symposium Series on Computational Intelligence(SSCI).IEEE,2019:96-103. [9] YE G Q,LI W G,WAN H.Study of RBF Neural Network Based on PSO Algorithm in Nonlinear System Identification[C]//2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA).IEEE,2015:852-855. [10] ZOU Z N,PENG H,LIU L,et al.Deep Convolutional Mesh RNN for Urban Traffic Passenger Flows Prediction[C]//2018 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Cloud & Big Data Computing Internet of People and Smart City Innovation.2018:1305-1310. [11] WANG J Y,HU F,XU X F,et al.A Deep Prediction Model of Traffic Flow Considering Precipitation Impact[C]//2018 International Joint Conference on Neural Networks (IJCNN).2018:1-7. [12] SHI X J,ZHANG W G,ZHANG Y,et al.Application of RBF Neural Network Based on PSO Algorithm in Fault Diagnosis of Actuation System[J].Journal of Naval Aeronautical and Astronautical University,2011,26(2):131-135. [13] LI B,SUN X X,LI S B,et al.Improved Particle Swarm Optimization Based on Genetic Hybrid Genes[J].Computer Engineering,2008,34(2):181-183. [14] CHANG C C,HUANG H T.Automatic Tuning of the RBF Kernel Parameter for Batch-Mode Active Learning Algorithms:ASca-lable Framework[J].IEEE Transactions on Cybernetics,2019,49(12):4460-4472. [15] YU W X,LIU L,ZHANG W C.Traffic Prediction MethodBased on RBF Neural Network with Improved Artificial Bee Colony Algorithm[C]//2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS).IEEE,2015:141-144. [16] YI H,JUNG H,BAE S.Deep neural networks for traffic flow prediction[C]// 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).2017:28-331. [17] ABADI M,BARHAM P,CHEM J,et al.Tensorflow:A system for large-scale machine learning[J].OSDI,2016,16(1):265-283. [18] ZHU L,YU F R,WANG Y G,et al.Big Data Analytics in Intelligent Transportation Systems:A Survey[J].IEEE Trransactions on Intelligent Transportation Systems,2019,20(1):383-398. [19] QIAN Y L,ZHANG H,PENG D G,et al.Fault diagnosis for generator unit based on RBF neural network optimized by GA-PSO[C]//Eighth International Conference on Natural Computation.IEEE,2012:233-236. [20] SONG L W,PENG M F,TIAN C L,et al.Analog circuit diagnosis based on particle swarm optimization radial basis function network[J].Application Research of Computers,2012,29(1):72-74. [21] CAO L H,LIU X L,GUO X D,et al.The Application of Rought Set and Improved QPSO-RBF Algorithm to Fault Diagnosis for Diesel Engine Valve[J].Information and Control,2011,40(4):570-576. [22] YANG X,HU D W.City traffic flow breakdown predictionbased on fuzzy rough set[J].Open Physics,2017,15(1):292-299. [23] MOU H,WANG C Y,GU J.Short-term Load ForecastingMethod Based on k-means Clustering and Varied Quantile outlierRobust Extreme Learning Machine[J].Journal of PowerScience and Technology,2016,31(3):51-56. [24] CHEN J,HE L,QUAN Y,et al.Application of BP Neural Networks based on genetic simulated annealing algorithm for shortterm electricity price forecasting[C]//International Conference on Advances in Electrical Engineering.IEEE,2014:1-6. [25] KUCUKOGLU I,DEWIL R,CATTRYSSE D.Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates [J].Expert Systems with Applications,2019,134(11):279-303. [26] ZHANG P,ZHOU X,PELLICCIONE P,et al.RBF-MLMR:A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network[J].IEEE Access,2017,5(1):21791-21805. [27] DONG J,LI Y J,WANG M.Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm[J].Applied Sciences,2019,9(13):2589. [28] HUANG J Z,XIE J,GAO Q H,et al.Study on RBF neural net-work method with application based on SA-HHGA optimization algorithm [J].Computer Engineering and Applications,2013,20(2):127-148. [29] SHENG Z,WANG J,ZHOU S,et al.Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search withsi-mulated annealing algorithm[J].Chaos (Woodbury,N.Y.),2014,24(1):013133. [30] ZOUHAIR C,NOREDDINE A,KHALID M,et al.A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network[C]//2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT).IEEE,2018:1-6. |
| [1] | 宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053 |
| [2] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
| [3] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [4] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
| [5] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
| [6] | 王润安, 邹兆年. 基于物理操作级模型的查询执行时间预测方法 Query Performance Prediction Based on Physical Operation-level Models 计算机科学, 2022, 49(8): 49-55. https://doi.org/10.11896/jsjkx.210700074 |
| [7] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
| [8] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [9] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
| [10] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [11] | 齐秀秀, 王佳昊, 李文雄, 周帆. 基于概率元学习的矩阵补全预测融合算法 Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning 计算机科学, 2022, 49(7): 18-24. https://doi.org/10.11896/jsjkx.210600126 |
| [12] | 杨炳新, 郭艳蓉, 郝世杰, 洪日昌. 基于数据增广和模型集成策略的图神经网络在抑郁症识别上的应用 Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition 计算机科学, 2022, 49(7): 57-63. https://doi.org/10.11896/jsjkx.210800070 |
| [13] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
| [14] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
| [15] | 刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179 |
|
||
首页