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A modified Hopfield neural networks model for graphs-based kinematic structure design

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Abstract

Graph theory can be used efficiently for both kinematic and dynamics analysis of mechanical structures. One of the most important and difficult issues in graphs theory-based structures design is graphs isomorphism discernment. The problem is vital for graph theory-based kinematic structures enumeration, which is known to be nondeterministic polynomial-complete problem. To solve the problem, a Hopfield neural networks (HNN) model is presented and some operators are improved to prevent premature convergence. By comparing with genetic algorithm, the computation times of the HNN model shows less affection when the number of nodes were enhanced. It is concluded that the algorithm presented in this paper is efficient for large-scale graphs isomorphism problem.

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Acknowledgments

The author would like to acknowledge the support of the National Science Foundation of China, Doctoral Candidate of JiangSu Province (No. CX07B_069z), the Natural Science Foundation of GuangXi province of China, the support program for Young and Middle-aged Disciplinary Leaders in Gangxi Higher Education Institution and Natural Science Foundation for Qualified Personnel of JiangSu University during the course of this work.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Wenzhou University, 325035, Wenzhou, People’s Republic of China

    Miao Zhang, Ningbo Liao & Chen Zhou

  2. Research Center of Micro-Nano Science and Technology, Jiangsu University, 212013, Zhenjiang, People’s Republic of China

    Ningbo Liao

Authors
  1. Miao Zhang
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  2. Ningbo Liao
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  3. Chen Zhou
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Correspondence to Miao Zhang.

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Zhang, M., Liao, N. & Zhou, C. A modified Hopfield neural networks model for graphs-based kinematic structure design. Engineering with Computers 26, 75–80 (2010). https://doi.org/10.1007/s00366-009-0153-2

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  • DOI: https://doi.org/10.1007/s00366-009-0153-2

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