Swarm intelligence algorithm for interconnect model order reduction with sub-block structure preserving

References

• Antoulas, A.C., & Sourensen, D.C. (2001). Approximation of large-scale dynamical systems: An overview. International Journal of Applied Mathematics and Computer Science, 11(5), 1093–1121.
   Google Scholar
• Bai, Z., Li, R., & Su, Y. (2008). A unified Krylov projection framework for structure-preserving model reduction. In H.-G. Bock, F. de Hoog, A. Friedman, A. Gupta, H. Neunzert, W.R. Pulleyblank, T. Rusten, F. Santosa, & A.-K. Tornberg (Eds.), Model order reduction: Theory, research aspects and applications (pp. 75–94). Berlin: Springer-Verlag.
   Google Scholar
• Beni, G., & Wang, J. (1989). Swarm intelligence in cellular robotic systems. In Proceedings of NATO Advanced Workshop on Robots and Biological Systems (pp. 703–712). Berlin: Springer.
   Google Scholar
• Benner, P., Hinze, M., & Jan, E. (2011). The need for novel model order reduction techniques in the electronics industry. In P. Benner, M. Hinze, & E.J.W. ter Maten (Eds.), Model reduction for circuit simulation (pp. 3–23). Berlin: Springer-Verlag.
   Google Scholar
• Cheng, S.L., & Hwang, C. (2001). Optimal approximation of linear systems by a differential evolution algorithm. IEEE Transactions on Systems, Man and Cybernetics - Part A, 31(6), 698–707.
   Web of Science ®Google Scholar
• Chu, C.C., Lee, H.J., Wu, S., & Lao, M.H. (2005). Interconnect model reduction by using the AORA algorithm with considering the adjoint network. In IEEE International Symposium on Circuits and Systems (pp. 1278–1281). Kobe: IEEE.
   Google Scholar
• Deepa, S.N., & Sugumaran, G. (2010). A modified particle swarm optimization for lower model formulation of linear time invariant continuous systems. In 2010 International Conference on Innovative Computing Technologies (pp. 1–5). Tamil Nadu: IEEE.
   Google Scholar
• Fawzi, M.A., & Bessam, Z.A.J. (2010). Nodal analysis techniques. In Element stamp algorithm for matrix formulation of Symbolic circuits (pp. 21–54). New York, NY: Nova Science Publishers, Inc.
   Google Scholar
• Feldmann, P., & Freund, R.W. (1995). Efficient linear circuit analysis by pade approximation via the lanczos process. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 14(5), 639–649.
   Web of Science ®Google Scholar
• Freund, R.W. (2004). SPRIM: Structure-preserving reduced-order interconnect macromodeling. In Proceedings of the International Conference on Computer Aided Design (pp. 80–87). Los Alamitos, CA: IEEE/ACM.
   Google Scholar
• Gallehdari, Z., Karrari, M., & Malik, O.P. (2009). Model order reduction using PSO algorithm and it's application to power systems. In International Conference on Electric Power and Energy Conversion Systems (pp. 1–5). Sharjah: IEEE.
   Google Scholar
• Hwang, C., & Hwang, J.H. (1996). A new two-step iterative method for optimal reduction of linear SISO systems. Journal of The Franklin Institute, 333(5), 631–645.
   Web of Science ®Google Scholar
• Jiang, Y.L., & Chen, H.B. (2012). Time domain model order reduction of general orthogonal polynomials for linear input-output systems, IEEE Transactions on Automatic Control, 57(2), 330–343.
   Web of Science ®Google Scholar
• Kerns, K.J., & Yang, A.T. (1998). Stable and efficient reduction of large, multiport RC network by pole analysis via congruence transformations. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 16(7), 734–744.
   Web of Science ®Google Scholar
• Li, M., Wang, D.B., & Zhen, Z.Y. (2010). Model reduction based on improved hybrid particle swarm optimization. In 2010 Chinese Control and Decision Conference (pp. 3365–3369). Xuzhou: IEEE.
   Google Scholar
• Luus, R. (1999). Optimal reduction of linear systems. Journal of The Franklin Institute, 336(3), 523–532.
   Web of Science ®Google Scholar
• Mi, N., Tan, S.X.-D, & Yan, B. (2009). Multiple block structure-preserving reduced order modeling of interconnect circuit. Integration – The VLSI Journal, 42(2), 158–168.
   Web of Science ®Google Scholar
• Mi, N., Yan, B., Tan, S.X.-D., Fan, J., & Yu, H. (2007). General block structure-preserving reduced order modeling of linear dynamic circuits. In International Symposium on Quality Electronic Design(ISQED) (pp. 633–638). San Jose, CA: IEEE.
   Google Scholar
• Odabasioglu, A., Celik, M., & Pileggi, L. (1998). PRIMA: Passive reduced-order interconnect macromodeling algorithm. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 17(8), 645–654.
   Web of Science ®Google Scholar
• Pillage, L.T., & Rohrer, R.A. (1990). Asymptotic waveform evaluation for timing analysis. IEEE Transactions on Comput-Aided Design of Integrated Circuits and Systems, 9(4), 352–366.
   Web of Science ®Google Scholar
• Salim, R., & Bettaveb, M. (2009). H2 optimal model reduction using genetic algorithms and particle swarm optimization. In ISMA'09 6^th International Symposium on Mechatronics and its Applications (pp. 1–6). Sharjah: IEEE.
   Google Scholar
• Silveira, M., Kamon, M., Elfadel, I., & White, J. (1996). A coordiante-transformed Arnoldi algorithm for generating guaranteed stable reduced-order models of RLC circuits. In Proceedings of the International Conference on Computer Aided Design (pp. 288–294). San Jose, CA: IEEE.
   Google Scholar
• Sivanandam, S.N., & Deepa, S.N. (2007). A genetic algorithm and particle swarm optimization approach for lower modeling of linear time invariant discrete systems. In International Conference on Computational Intelligence and Multimedia Application (pp. 443–447). Sivakasi: IEEE.
   Google Scholar
• Villena, J.F., Schilder, W.H.A., & Silveira, L.M. (2009). Block oriented model order reduction of interconnected systems. International Journal of Numerical Modelling: Electronic Networks Devices and Fields, 2009, 1–19.
   Google Scholar
• Wang, J.M., Chu, C., Yu, Q., & Kuh, E.S. (2002). On projection-based algorithms for model-order reduction of interconnects. IEEE Transactions on Circuits and Systems I, 49(11), 1563–1585.
   Google Scholar
• Wang, L. (2001). Intelligent optimization algorithms with applications. Beijing: Tsinghua University & Springer Press.
   Google Scholar
• Wang, Z.W., & Liu, X.Q. (2006). Model order reduction based on particle swarm optimization. In 2006 8^th International Conference on Signal Processing (pp. 16–20). Beijing: IEEE.
   Google Scholar
• Wilhelmus, S., Henk, A., Vorst, V.D., & Joust, R. (2008). Introduction to model order reduction. In L.L. Bonilla, R. Mattheij, H. Neunzert, & O. Scherzer (Eds.), Model order reduction: Theory, research aspects and applications (pp. 3–32). Berlin: Springer-Verlag.
   Google Scholar
• William, F.T. (1965). An algorithm for the inversion of finite Hankel matrices. Journal of Society for Industrial and Applied Mathematics, 13(4), 1102–1107.
   Web of Science ®Google Scholar
• Yang, F., Zeng, X., Su, Y., & Zhou, D. (2007). RLCSYN: RLC equivalent circuit synthesis for structure-preserved reduced-order model of interconnect. In IEEE International Symposium on Circuits and Systems (pp. 2710–2713). New Orleans, LA: IEEE.
   Google Scholar
• Younes, C., & Paul, V.D. (2002). A collection of benchmark examples for model reduction of linear time invariant dynamical systems. SLICOT Working Note 2002-2. Leuven: SLICOT.
   Google Scholar
• Yu, H., He, L., & Tan, S.X.D. (2005). Block structure preserving model reduction for linear circuits with large numbers of ports. In Proceedings of IEEE International Workshop on Behavioral Modeling and Simulation (pp. 1–6). San Jose, CA: IEEE.
   Google Scholar