Model reduction of linear networks with nonlinear elements

In this paper a method for the reduction of linear networks with a large number of nonlinear elements is presented. In the standard approach the nonlinear elements are extracted from the linear part and the linear part is reduced. A large number of nonlinear elements leads to a large number of ports for the interconnection of linear and nonlinear network parts which is a strong limitation for the reduction. A method for the reduction of the number of ports of the linear part which enables a more efficient model order reduction with standard projection techniques is presented. The method is based on decomposition of the equations of the nonlinear elements which are explicitly described by one current or voltage function. By reducing an example model the method is illustrated and validated.

[1]  Sheldon X.-D. Tan,et al.  TermMerg: An Efficient Terminal-Reduction Method for Interconnect Circuits , 2007, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  Ibrahim M. Elfadel,et al.  A block rational Arnoldi algorithm for multipoint passive model-order reduction of multiport RLC networks , 1997, 1997 Proceedings of IEEE International Conference on Computer Aided Design (ICCAD).

[3]  Joel R. Phillips,et al.  Poor man's TBR: a simple model reduction scheme , 2004 .

[4]  Fan Yang,et al.  RLCSYN: RLC Equivalent Circuit Synthesis for Structure-Preserved Reduced-order Model of Interconnect , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[5]  João M. S. Silva,et al.  Outstanding Issues in Model Order Reduction , 2007 .

[6]  João M. S. Silva,et al.  Issues in Model Reduction of Power Grids , 2005, VLSI-SoC.

[7]  L. Chua Nonlinear circuits , 1984 .

[8]  J. Phillips,et al.  Poor man's TBR: a simple model reduction scheme , 2005 .

[9]  Y. Zhou,et al.  On the decay rate of Hankel singular values and related issues , 2002, Syst. Control. Lett..

[10]  Athanasios C. Antoulas,et al.  Approximation of Linear Dynamical Systems , 1998 .

[11]  Wolfgang Mathis,et al.  Efficient model reduction of passive electrical networks with a large number of independent sources , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[12]  J. Roos,et al.  Comparison of reduced-order interconnect macromodels for time-domain simulation , 2004 .

[13]  V. Tikhomirov On the Representation of Continuous Functions of Several Variables as Superpositions of Continuous Functions of one Variable and Addition , 1991 .

[14]  Peter Feldmann,et al.  Model order reduction techniques for linear systems with large numbers of terminals , 2004, Proceedings Design, Automation and Test in Europe Conference and Exhibition.

[15]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[16]  Joel R. Phillips,et al.  Projection-based approaches for model reduction of weakly nonlinear, time-varying systems , 2003, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[17]  Lawrence T. Pileggi,et al.  PRIMA: passive reduced-order interconnect macromodeling algorithm , 1997, ICCAD 1997.

[18]  I. Elfadel,et al.  A block rational Arnoldi algorithm for multipoint passive model-order reduction of multiport RLC networks , 1997, ICCAD 1997.

[19]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[20]  Kiyotaka Yamamura,et al.  An algorithm for representing functions of many variables by superpositions of functions of one variable and addition , 1996 .