A kinematic chain isomorphism identification algorithm using optimized circuit simulation method

Similar Vertices and Isomorphism Detection for Planar Kinematic Chains Based on Ameliorated Multi-Order Adjacent Vertex Assignment Sequence

“…Step 4: There are two corresponding relations by comparing S 7 a,1 with S 7 b,1,1 and S 7 b,1,2 : b a M 1 = {28, 29, 30, 9, 8, 1, 2, 3, 4, 5, 6, 7, 14, 16, 17, 18, 19, 20, 21, 26, 25, 24, 23, 27, 15, 13, 11, 12, 10, 22}, b a M 2 = {28, 29,30,9,8,1,2,3,4,5,6,7,14,16,17,18,19,20,25,26,21,22,23,27,15,13,11,12, 10, 24}.…”

“…By using this method, the sufficiency of isomorphism detection was obtained based on vertex correspondence. Other isomorphism detection methods, such as partition matching algorithm [13], hybrid immune algorithm [14], optimized circuit simulation [15][16][17], connectivity number and entropy [18], binary code [19], information theory [20], joint matrix [21,22], loop theory [23][24][25][26], high-order adjacency link values [27], are noticeable too. Although all the abovementioned synthesis methods can identify the isomorphism, they cannot accurately find the similar vertices that are beneficial for mechanism selection and isomorphism detection.…”

Improved high-order adjacent vertex assignment sequence for similarity vertices and isomorphism identification of planar kinematic chains

“…Yang et al [19] proposed a hybrid immune algorithm by combining immune algorithm, genetic algorithm and local search algorithm, and realized the recognition of isomorphism by using this algorithm. Shang et al [20] proposed to use the method of optimized circuit simulation to identify the KCs isomorphism. In 2018, Sun et al [21] proposed to use joint matrix to uniquely describe the structure of KCs, obtain the corresponding component and joint information from joint matrix, and use joint matrix, component and joint to identify isomorphism.…”