Efficient Substructure Preserving MOR Using Real-Time Temporal Supervised Neural Network

  • Othman M. K. Alsmadi
  • Zaer. S. Abo-Hammour
  • Adnan M. Al-Smadi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


This paper addresses a novel model order reduction (MOR) technique with dominant substructure preservation. This process leads to cost minimization of the considered physical system which could be of any type from motors to circuitry packaging to software design. The new technique is formulated based on an artificial neural network (ANN) transformation along with the linear matrix inequality (LMI) optimization method. The proposed method is validated by comparing its performance with the following well-known reduction techniques Balanced Schur Decomposition (BSD) and state elimination via balanced realization.


Neural networks model order reduction 


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  1. 1.
    Rudnyi, E.B., Korvink, J.G.: Model Order Reduction of MEMS for Efficient Computer Aided Design and System Simulation. In: 16th International Symposium on Mathematical Theory of Networks and Systems, Leuven, Netherlands, pp. 1–6 (2004)Google Scholar
  2. 2.
    Ramesh, K., Ayyar, K., Nirmalkumar, A., Gurusamy, G.: Design of Current Controller for Two Quadrant DC Motor Drive by Using Model Order Reduction Technique. International Journal of Computer Science and Information Security 7(1), 17–23 (2010)Google Scholar
  3. 3.
    Antoulas, A.: Approximation of large-scale dynamical systems, advances in design and control. SIAM, Philadelphia (2005)Google Scholar
  4. 4.
    Freund, R.: SPRIM: Structure-preserving reduced-order interconnect macro-modeling. In: IEEE/ACM ICCAD (2004)Google Scholar
  5. 5.
    Fujimoto, K., Scherpen, J.M.A.: Balancing and Model Reduction for Discrete-Time Nonlinear Systems based on Hankel Singular Value Analysis. In: Proc. MTNS 2004, Leuven, Belgium, pp. 343–347 (2004)Google Scholar
  6. 6.
    Haykin, S.: Neural Networks: a Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)zbMATHGoogle Scholar
  7. 7.
    Rabiei, P., Pedram, M.: Model-order reduction of large circuits using balanced truncation. In: Proc. IEEE ASP-DAC, pp. 237–240 (1999)Google Scholar
  8. 8.
    Safonov, M., Chiang, Y.: A Schur Method for Balanced-Truncation Model Reduction. IEEE Trans. on Automatic Control. 34(7), 729–733 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Varga, A., Anderson, B.D.O.: Accuracy enhancing method for the frequency-weighted balancing related method reduction. In: Proc. CDC 2001, Orlando, Florida, pp. 3659–3664 (2001)Google Scholar
  10. 10.
    Heydari, P., Pedram, M.: Model-Order Reduction Using Variational Balanced Truncation with Spectral Shaping. IEEE Transactions on Circuits and Systems I 53(4), 879–891 (2006)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Boyd, S., El Ghaoui, L., Feron, E., Balakrishnan, V.: Linear Matrix Inequalities in System and Control Theory. Society for Industrial and Applied Mathematics (SIAM), Philadelphia (1994)zbMATHGoogle Scholar
  12. 12.
    Iracleous, D., Alexandridis, A.: A simple Solution to the Optimal Eigenvalue assignment Problem. IEEE Trans. Act. Auto. Cont. 9(44), 1746–1749 (1999)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Othman M. K. Alsmadi
    • 1
  • Zaer. S. Abo-Hammour
    • 2
  • Adnan M. Al-Smadi
    • 3
  1. 1.Department of Electrical EngineeringUniversity of JordanAmmanJordan
  2. 2.Department of Mechatronics EngineeringUniversity of JordanAmmanJordan
  3. 3.Department of Computer ScienceAl Al-Bayt UniversityMafraqJordan

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