Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bengio, Y., Frasconi, P.: Input-Output HMM’s for Sequence Processing. IEEE Trans. on Neural Networks 7(5), 1231–1249 (1996)
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)
Wan, V., Renals, S.: Speaker verification using sequence discriminant support vector machines. IEEE Trans. on Speech and Audio Processing 13(2), 203–210 (2005)
Li, Y.: Hidden Markov models with states depending on observations. Pattern Recognition Letters 26, 977–984 (2005)
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)
Chamroukhi, F.: Hidden process regression for curve modeling, classification and tracking. Dissertation, Université de Technologie de Compiègne (2010)
Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401–422 (2004)
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)
Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot Dynamics and Control. John Wiley and Sons (2004)
Corke, P.I.: Robotics Toolbox for Matlab, http://www.petercorke.com
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, Q., Zhao, X., Han, J. (2013). A General Closed-Loop Framework for Multi-dimensional Sequence Processing. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-33932-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33931-8
Online ISBN: 978-3-642-33932-5
eBook Packages: EngineeringEngineering (R0)