CLC number: TN431
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-11-13
Cited: 0
Clicked: 6894
Xiao-hua Li, Ji-zhong Shen. An algorithm for identifying symmetric variables in the canonical OR-coincidence algebra system[J]. Journal of Zhejiang University Science C, 2014, 15(12): 1174-1182.
@article{title="An algorithm for identifying symmetric variables in the canonical OR-coincidence algebra system",
author="Xiao-hua Li, Ji-zhong Shen",
journal="Journal of Zhejiang University Science C",
volume="15",
number="12",
pages="1174-1182",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1400093"
}
%0 Journal Article
%T An algorithm for identifying symmetric variables in the canonical OR-coincidence algebra system
%A Xiao-hua Li
%A Ji-zhong Shen
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 12
%P 1174-1182
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1400093
TY - JOUR
T1 - An algorithm for identifying symmetric variables in the canonical OR-coincidence algebra system
A1 - Xiao-hua Li
A1 - Ji-zhong Shen
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 12
SP - 1174
EP - 1182
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1400093
Abstract: To simplify the process for identifying 12 types of symmetric variables in the canonical OR-coincidence (COC) algebra system, we propose a new symmetry detection algorithm based on OR-NXOR expansion. By analyzing the relationships between the coefficient matrices of sub-functions and the order coefficient subset matrices based on OR-NXOR expansion around two arbitrary logical variables, the constraint conditions of the order coefficient subset matrices are revealed for 12 types of symmetric variables. Based on the proposed constraints, the algorithm is realized by judging the order characteristic square value matrices. The proposed method avoids the transformation process from OR-NXOR expansion to AND-OR-NOT expansion, or to AND-XOR expansion, and solves the problem of completeness in the dj-map method. The application results show that, compared with traditional methods, the new algorithm is an optimal detection method in terms of applicability of the number of logical variables, detection type, and complexity of the identification process. The algorithm has been implemented in C language and tested on MCNC91 benchmarks. Experimental results show that the proposed algorithm is convenient and efficient.
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