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Symbolic Sensitivity Analysis Enhanced by Nullor Model and Modified Coates Flow Graph

  • Irina Asenova
  • Franciszek Balik
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 479)

Abstract

In this chapter it was shown, how such the pathological elements like nullors can be exploited to symbolic sensitivity analysis enhancement. A method of first-, second-order and multiparameter symbolic sensitivity determination based on the nullor model of active devices and modified Coates flow graph is presented. The method performs symbolic sensitivity analysis with respect to various circuit parameters appeared not only at one location in the nullor model, respectively in the modified Coates flow graph. Illustrative examples on symbolic sensitivity analysis are given. In symbolic sensitivity analysis very important role plays the number of additionally generated expressions and in consequence additional number of arithmetical operations. The main drawback of some methods based on the adjoint graph or on the two-graph technique, i.e. the necessity to multiply analyze the corresponding graph, is avoided. Advantages of the method suggested are that, the matrix inversion is not required and due to the modified Coates graph this method is significantly simplified. Simplifications of the method introduced lead to the significant reduction of the final symbolic expressions without violation of accuracy. This simplification method can be considered as SBG–type (Simplification Before Generation) and has an important impact on symbolic analysis. In the chapter, it was shown that the presented method is more effective than the transimpedance method taking the number of arithmetical operations and the circuit insight into consideration. Comparison results for the multiparameter sensitivity calculations of the voltage transfer function for a fourth-order low pass filter and a second-order high pass filter are presented.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Communication and Electrical EquipmentUniversity of Transport “T. Kableshkov”SofiaBulgaria
  2. 2.Jan Wyżykowski UniversityPolkowicePoland

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