Title: An adaptive neural networks model for isomorphism discernment of large-scale kinematic structure

Authors: Miao Zhang, Ningbo Liao, Chen Zhou

Addresses: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China. ' College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China; Laboratory of Materials and Micro-Structural Integrity, Jiangsu University, Zhenjiang 212013, PR China. ' College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China

Abstract: Graphs isomorphism discernment is one of the most important and difficult issues in graphs theory based structures design. To solve the problem, a Hopfield Neural Networks (HNN) model is presented. The solution of HNN is design as a permutation matrix of two graphs, and some operators are improved to prevent premature convergence. The convergence properties of HNN model and the improved HNN model are studied by analysing the search process. The computation times of the HNN model will not be affected greatly by enhancing the number of nodes in graph and the algorithm is efficient for large-scale graphs isomorphism problem.

Keywords: HNN; Hopfield neural networks; graph isomorphism; kinematic structures; structure enumeration; intelligent design; graph theory.

DOI: 10.1504/IJMPT.2010.035809

International Journal of Materials and Product Technology, 2010 Vol.39 No.3/4, pp.347-356

Published online: 05 Oct 2010 *

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