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Neurocomputing method based on structural finite element analysis of discrete model

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Abstract

Based on structural finite element analysis of discrete models, a neurocomputing strategy is developed in this paper. Dynamic iterative equations are constructed in terms of neural networks of discrete models. Determination of the iterative step size, which is important for convergence, is investigated based on the positive definiteness of the finite element stiffness matrix. Consequently, a method of choosing the step size of dynamic equations is proposed and the computational formula of the best step size is derived. The analysis of the computing model shows that the solution of finite element system equations can be obtained by the method of neural network computation efficiently. The proposed method can be used for parallel computation of structural finite element in a large-scale integrated circuit (LSI).

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Acknowledgments

This research was partially supported by the National Natural Science Foundation of China under the contract number 50775026 and Higher education research projects in Inner Mongolia of the contract number NJZY08058. Constructive suggestions and comments from the referees and editors are very much appreciated. The authors would also like to acknowledge constructive revision suggestions and help with English by Professor Hae Chang Gea, Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey and Dr. Christopher Hoyle, Department of Mechanical Engineering, Northwestern University, USA.

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Authors and Affiliations

  1. College of Science, Inner Mongolia University of Technology, 010051, Huhhot, China

    Haibin Li & Wei Duan

  2. School of Mechanical, Electronic, and Industrial Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, Sichuan, China

    Hong-Zhong Huang

Authors
  1. Haibin Li
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  2. Wei Duan
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  3. Hong-Zhong Huang
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Correspondence to Hong-Zhong Huang.

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Li, H., Duan, W. & Huang, HZ. Neurocomputing method based on structural finite element analysis of discrete model. Neural Comput & Applic 19, 875–882 (2010). https://doi.org/10.1007/s00521-010-0353-0

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  • DOI: https://doi.org/10.1007/s00521-010-0353-0

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