Two-timescale projection neural networks in collaborative neurodynamic approaches to global optimization and distributed optimization.

Journal: Neural networks : the official journal of the International Neural Network Society
PMID: 37864998

Abstract

In this paper, we propose a two-timescale projection neural network (PNN) for solving optimization problems with nonconvex functions. We prove the convergence of the PNN with sufficiently different timescales to a local optimal solution. We develop a collaborative neurodynamic approach with multiple such PNNs to search for global optimal solutions. In addition, we develop a collaborative neurodynamic approach with multiple PNNs connected via a directed graph for distributed global optimization. We elaborate on four numerical examples to illustrate the characteristics of the approaches.

Authors

  • Banghua Huang
    School of Mathematical Sciences, Zhejiang Normal University, JinhuaZhejiang 321004, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Yun-Liang Jiang
    School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang, 321004, China; School of Information Engineering, Huzhou University, Huzhou, Zhejiang, 313000, China.
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.

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