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A varying-parameter fixed-time gradient-based dynamic network for convex optimization.

Dan Wang1, Xin-Wei Liu2

  • 1School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gradient-based dynamic network for convex optimization, achieving faster fixed-time convergence and enhanced robustness against noise interference. The new network demonstrates superior performance compared to existing models.

Keywords:
Activation functionFixed-time convergenceGradient-based dynamic networkTime-varying scaling parameterUnbounded noise

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Area of Science:

  • Optimization Theory
  • Neural Network Design
  • Convex Analysis

Background:

  • Gradient-based dynamic networks are crucial for solving convex optimization problems.
  • Existing fixed-time convergence networks struggle with noise interference.
  • Improving convergence speed and noise resilience is a key challenge.

Purpose of the Study:

  • To develop a gradient-based dynamic network with improved fixed-time convergence.
  • To enhance the network's robustness against both bounded and unbounded noises.
  • To achieve a smaller upper bound on convergence time.

Main Methods:

  • Design of a novel activation function.
  • Proposal of a new gradient-based dynamic network architecture.
  • Incorporation of a time-varying scaling parameter to accelerate convergence.

Main Results:

  • The proposed network achieves fixed-time convergence with a reduced upper bound.
  • Demonstrated robustness against bounded noise interference.
  • Capability to resist interference from unbounded noises.
  • Numerical tests confirm effectiveness and superiority over existing methods.

Conclusions:

  • The novel gradient-based dynamic network offers significant improvements in convergence speed and noise robustness for convex optimization.
  • The designed activation function and time-varying parameter are key to the enhanced performance.
  • This work advances the field of dynamic networks for solving complex optimization problems.