2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

交通流量VNNTF神经网络模型多步预测研究

殷礼胜 何怡刚 董学平 鲁照权

殷礼胜, 何怡刚, 董学平, 鲁照权. 交通流量VNNTF神经网络模型多步预测研究. 自动化学报, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066
引用本文: 殷礼胜, 何怡刚, 董学平, 鲁照权. 交通流量VNNTF神经网络模型多步预测研究. 自动化学报, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066
YIN Li-Sheng, HE Yi-Gang, DONG Xue-Ping, LU Zhao-Quan. Research on the Multi-step Prediction of Volterra Neural Network for Traffic Flow. ACTA AUTOMATICA SINICA, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066
Citation: YIN Li-Sheng, HE Yi-Gang, DONG Xue-Ping, LU Zhao-Quan. Research on the Multi-step Prediction of Volterra Neural Network for Traffic Flow. ACTA AUTOMATICA SINICA, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066

交通流量VNNTF神经网络模型多步预测研究

doi: 10.3724/SP.J.1004.2014.02066
基金项目: 

国家杰出青年科学基金(50925727),教育部科学技术研究重大项目(313018),安徽省高校自然科学基金重点项目(KJ2012A219),中国博士后科学基金(2013M541823)资助

详细信息
    作者简介:

    殷礼胜 博士,合肥工业大学电气与自动化工程学院副教授.2007年获得重庆大学控制科学与工程专业博士学位.主要研究方向为混沌理论,交通流,神经网络,现代智能算法.本文通信作者.E-mail:yls20000@163.com

    通讯作者:

    殷礼胜 博士,合肥工业大学电气与自动化工程学院副教授.2007年获得重庆大学控制科学与工程专业博士学位.主要研究方向为混沌理论,交通流,神经网络,现代智能算法.本文通信作者.E-mail:yls20000@163.com

Research on the Multi-step Prediction of Volterra Neural Network for Traffic Flow

Funds: 

Supported by National Natural Science Funds of China for Distinguished Young Scholar (50925727), (Key Grant) Project of Chinese Ministry of Education (313018), Natural Science Foundation of Univ ersities of Anhui Province (KJ2012A219), and China Postdoctoral Science Foundation (2013M541823)

  • 摘要: 研究了VNNTF 神经网络(Volterra neural network trafficflow model,VNNTF) 交通流量混沌时间序列多步预测问题. 通过分析比较交通流量混沌时间序列相空间重构的嵌入维数和Volterra 离散模型之间的关系,给出了确定交通流量Volterra 级数模型截断阶数和截断项数的方法,并在此基础上建立了VNNTF 神经网络交通流量时间序列模型;设计了交通流量Volterra 神经网络的快速学习算法;最后,利用交通流量混沌时间序列对VNNTF 网络模型,Volterra 预测滤波器和BP 网络进行了多步预测实验,比较了多步预测结果的仿真图、绝对误差的柱状图以及归一化后的方均根;实验结果表明VNNTF 神经网络的多步预测性能明显优于Volterra 预测滤波器和BP 神经网络.
  • [1] Xiao Z L, Jing X J, Cheng L. Parameterized convergence bounds for Volterra series expansion of NARX models. IEEE Transactions on Signal Processing, 2013, 61(20): 5026-5038
    [2] Zhu Xiong-Yong, Zhou Jie, Tan Hong-Zhou. Method for eliminating LCD motion de-blurring model's pole. Acta Automatica Sinica, 2012, 38(5): 759-768 (朱雄泳, 周杰, 谭洪舟. 一种消除 LCD 运动图像去模糊模型极点的方法. 自动化学报, 2012, 38(5): 759-768)
    [3] Asyali M, Alc M. Obtaining Volterra kernels from neural networks. In: Proceedings of World Congress on Medical Physics and Biomedical Engineering 2006, IFMBE Proceedings. Berlin: Springer Berlin Heidelberg, 2007. 11-15
    [4] Silveira D D, Gilabert P L, dos Santos A B, Gadringer M. Analysis of variations of volterra series models for RF power amplifiers. IEEE Microwave and Wireless Components Letters, 2013, 23(8): 442-444
    [5] Ghasemi M, Tavassoli K M, Babolian E. Numerical solutions of the nonlinear Volterra-Fredholm integral equations by using homotopy perturbation method. Applied Mathematics and Computation, 2007, 188(1): 446-449
    [6] Meng X Z, Chen L S. Permanence and global stability in an impulsive Lotka-Volterra n-species competitive system with both discrete delays and continuous delays. International Journal of Biomathematics, 2008, 1(2): 179-196
    [7] Despotovic V, Goertz N, Peric Z. Nonlinear long-term prediction of speech based on truncated Volterra series. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(3): 1069-1073
    [8] Chen F D. Permanence and global attractivity of a discrete multispecies Lotka-Volterra competition predator-prey systems. Applied Mathematics and Computation, 2006, 182(1): 3-12
    [9] Kobayakawa S, Yokoi H. Evaluation of prediction capability of Non-recursion type 2nd-order Volterra neuron network for electrocardiogram. In: Proceedings of the 15th International Conference on Neuro-Information Processing of the Asia Pacific Neural Network Assembly, Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2009, 5507: 679-686
    [10] Kang Ling, Wang Cheng, Jiang Tie-Bing. Hydrologic model of Volterra neural network and its application. Journal of Hydroelectric Engineering, 2006, 25(5): 22-26 (康玲, 王乘, 姜铁兵. Volterra神经网络水文模型及应用研究. 水力发电学报, 2006, 25(5): 22-26)
    [11] Rubiolo M, Stegmayer G, Milone D. Compressing arrays of classifiers using Volterra-neural network: application to face recognition. Neural Computing & Applications, 2013, 23(6): 1687-1701
    [12] Yu J L, Yi Z, Zhou J L. Fcontinuous attractors of Lotka-Volterra recurrent neural networks with infinite neurons. IEEE Transactions on Neural Networks, 2010, 21(10): 1690-1695
    [13] Jia Li, Yang Ai-Hua, Qiu Ming-Sen. Research on multi-signal based neuro-fuzzy Hammerstein-Wiener model. Acta Automatica Sinica, 2013, 39(5): 690-696 (贾立, 杨爱华, 邱铭森. 基于多信号源的神经模糊Hammerstein-Wiener 模型研究. 自动化学报, 2013, 39(5): 690-696)
    [14] Si Wei, Duan Zhe-Min, Wang Hai-Tao. Novel method based on projection of vectors in linear space to identify Volterra kernels of arbitrary orders. Application Research of Computers, 2008, 25(11): 3340-3342 (司伟, 段哲民, 王海涛. 基于线性空间投影的计算Volterra级数高阶核的方法. 计算机应用研究, 2008, 25(11): 3340-3342)
    [15] Li Peng-Hua, Chai Yi, Xiong Qing-Yu. Quantum gate Elman neural network and its quantized extended gradient back-propagation training algorithm. Acta Automatica Sinica, 2013, 39(9): 1511-1522 (李鹏华, 柴毅, 熊庆宇. 量子门Elman神经网络及其梯度扩展的量子反向传播学习算法. 自动化学报, 2013, 39(9): 1511-1522)
    [16] Zhao H Q, Zeng X P, He Z Y. Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network. IEEE Transactions on Neural Networks, 2011, 22(9): 1494-1507
    [17] Wu Yu-Xiang, Wang Cong. Deterministic learning based adaptive network control of robot in task space. Acta Automatica Sinica, 2013, 39(9): 806-815 (吴玉香, 王聪. 基于确定学习的机器人任务空间自适应神经网络控制. 自动化学报, 2013, 39(9): 806-815)
    [18] Yakubov Y A. On nonlinear Volterra equations of convolution type. Differential Equations, 2009, 45(9): 1326-1336
    [19] Murakami S, Ngoc P H A. On stability and robust stability of positive linear Volterra equations in Banach lattices. Central European Journal of Mathematics, 2010, 8(5): 966-984
    [20] Bibik Y V. The second Hamiltonian structure for a special case of the Lotka-Volterra equations. Computational Mathematics and Mathematical Physics, 2007, 47(8): 1285-1294
    [21] Yin L S, Huang X Y, Yang Z Y, Xiang C C. Prediction for chaotic time series based on discrete Volterra neural networks. In: Proceedings of the 3rd International Symposium on Neural Networks, Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2006, 3972: 759-764
  • 加载中
计量
  • 文章访问数:  1835
  • HTML全文浏览量:  100
  • PDF下载量:  1255
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-06-10
  • 修回日期:  2013-11-26
  • 刊出日期:  2014-09-20

目录

    /

    返回文章
    返回