Comparison of the Methods of Graphical Solution of Symbolic Sensitivity

Brtnik, Bohumil

doi:10.1109/radioelek.2019.8733555

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…In the past several decades, flow graph analysis has been widely used in electrical engineering [1][2][3][4][5][6], computer science, biological science [7], and for solving economic problems. Furthermore, the applications of graph theory in conjunction with symbolic network analysis [8,9] and the computer-aided simulation of electronic circuits [10] have been widely encountered in recent years.…”

Section: Introductionmentioning

confidence: 99%

A Matrix Approach for Analyzing Signal Flow Graph

2020

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Mason’s gain formula can grow factorially because of growth in the enumeration of paths in a directed graph. Each of the (n − 2)! permutation of the intermediate vertices includes a path between input and output nodes. This paper presents a novel method for analyzing the loop gain of a signal flow graph based on the transform matrix approach. This approach only requires matrix determinant operations to determine the transfer function with complexity O(n3) in the worst case, therefore rendering it more efficient than Mason’s gain formula. We derive the transfer function of the signal flow graph to the ratio of different cofactor matrices of the augmented matrix. By using the cofactor expansion, we then obtain a correspondence between the topological operation of deleting a vertex from a signal flow graph and the algebraic operation of eliminating a variable from the set of equations. A set of loops sharing the same backward edges, referred to as a loop group, is used to simplify the loop enumeration. Two examples of feedback networks demonstrate the intuitive approach to obtain the transfer function for both numerical and computer-aided symbolic analysis, which yields the same results as Mason’s gain formula. The transfer matrix offers an excellent physical insight, because it enables visualization of the signal flow.

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