A General Closed-Loop Framework for Multi-dimensional Sequence Processing

  • Qichuan Ding
  • Xingang Zhao
  • Jianda Han
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 194)


A novel closed-loop framework for multidimensional sequence processing is proposed in this paper. Traditional sequence-driven models are always forward, so no information is feedback to correct their outputs, which may deviate from the true values gradually due to the estimation error accumulating. To overcome the problem, the multidimensional vector in the input sequence is divided into two vectors based on its data attribute. One vector sequence generated from the original input sequence is considered as the new input sequence, and the other is considered as the measurement output sequence. The original output sequence is treated as the state sequence. Then, a closed-loop model in the state-space form is constructed, with which the states can be estimated online by filtering algorithms. The feasibility of the proposed framework has been verified by using the robot inverse kinematics.


sequence processing closed-loop framework state-space model robot inverse kinematics 


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  1. 1.
    Bengio, Y., Frasconi, P.: Input-Output HMM’s for Sequence Processing. IEEE Trans. on Neural Networks 7(5), 1231–1249 (1996)CrossRefGoogle Scholar
  2. 2.
    Guarneri, P., Rocca, G., Gobbi, M.: A Neural-Network-Based Model for the Dynamic Simulation of the Tire/Suspension System While Traversing Road Irregularities. IEEE Trans. on Neural Networks 19(9), 1549–1563 (2008)CrossRefGoogle Scholar
  3. 3.
    Wan, V., Renals, S.: Speaker verification using sequence discriminant support vector machines. IEEE Trans. on Speech and Audio Processing 13(2), 203–210 (2005)CrossRefGoogle Scholar
  4. 4.
    Li, Y.: Hidden Markov models with states depending on observations. Pattern Recognition Letters 26, 977–984 (2005)CrossRefGoogle Scholar
  5. 5.
    Jaeger, H.: Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the ‘echo state network’ approach, GMD Report 159, German National Research Center for Information Technology, pp. 1–48 (2002)Google Scholar
  6. 6.
    Chamroukhi, F.: Hidden process regression for curve modeling, classification and tracking. Dissertation, Université de Technologie de Compiègne (2010)Google Scholar
  7. 7.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401–422 (2004)CrossRefGoogle Scholar
  8. 8.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  9. 9.
    Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot Dynamics and Control. John Wiley and Sons (2004)Google Scholar
  10. 10.
    Corke, P.I.: Robotics Toolbox for Matlab,
  11. 11.
    Aangenent, W., Kostic, D., Jager, B.D., Molengraft, R.V.D., Steinbuch, M.: Data-Based Optimal Control. In: American Control Conference, pp. 1460–1465 (2005)Google Scholar
  12. 12.
    Cortez, P., Embrechts, M.J.: Opening Black Box Data Mining Models Using Sensitivity Analysis. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 341–348 (2011)Google Scholar
  13. 13.
    Frasconi, P., Gori, M., Sperduti, A.: A General Framework for Adaptive Processing of Data Structures. IEEE Trans. on Neural Networks 9(5), 768–786 (1998)CrossRefGoogle Scholar
  14. 14.
    Westwick, D.T., Perreault, E.J.: Closed-Loop Identification: Application to the Estimation of Limb Impedance in a Compliant Environment. IEEE Trans. on Biomedical Engineering 58(3), 521–530 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.State Key Laboratory of RoboticsShenyang Institute of Automation, Chinese Academy of SciencesShenyangChina
  2. 2.Graduate SchoolChinese Academy of SciencesBeijingChina

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