Fixed-time neural networks with time-invariant and time-varying coefficients for mixed variational inequalities

Hongsong WEN, Xing HE*, Jing XU, Mingliang ZHOU, Tingwen HUANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

This paper develops several fixed-time neural networks for solving mixed variational inequalities (MVIs). The proposed networks are highly efficient and with fixed-time convergence. First, based on the conventional forward-backward-forward neural network (FNN) and sliding mode control technique, a time-invariant fixed-time FNN (FxTFNN) is designed. Next, the Euclidean norm of FNN is introduced into FxTFNN to design the modified FxTFNN (MFxTFNN). It is shown that the proposed FxTFNN and MFxTFNN have fixed-time convergence properties and their settling-time functions are independent of the initial values. The proposed FxTFNN and MFxTFNN can be used to solve the Lasso problem and apply sparse signal reconstruction and image reconstruction. In addition, by introducing time-varying coefficients based on FxTFNN and MFxTFNN, time-varying FxTFNN (TFxTFNN) and time-varying MFxTFNN (TMFxTFNN) are developed. Finally, experimental results of numerical example, signal reconstruction, and image reconstruction are used to verify the effectiveness and superiority of the proposed neural networks.

Original languageEnglish
Article number120078
Number of pages15
JournalInformation Sciences
Volume659
Early online date4 Jan 2024
DOIs
Publication statusPublished - Feb 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Inc.

Keywords

  • Fixed-time convergence
  • Mixed variational inequalities (MVIs)
  • Sliding mode control technique
  • Sparse signal reconstruction
  • Time-varying coefficients

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