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Journal Articles AIMS Mathematics Year : 2023

Improved results on an extended dissipative analysis of neural networks with additive time-varying delays using auxiliary function-based integral inequalities

Saravanan Shanmugam
  • Function : Author
R Vadivel
  • Function : Author
Mohamed Rhaima
  • Function : Author
  • PersonId : 1316279
Hamza Ghoudi
  • Function : Author
  • PersonId : 1348812

Abstract

The issue of extended dissipative analysis for neural networks (NNs) with additive time-varying delays (ATVDs) is examined in this research. Some less conservative sufficient conditions are obtained to ensure the NNs are asymptotically stable and extended dissipative by building the agumented Lyapunov-Krasovskii functional, which is achieved by utilizing some mathematical techniques with improved integral inequalities like auxiliary function-based integral inequalities (gives a tighter upper bound). The present study aims to solve the H ∞ , L 2-L ∞ , passivity and (Q, S , R)-γ-dissipativity performance in a unified framework based on the extended dissipativity concept. Following this, the condition for the solvability of the designed NNs with ATVDs is presented in the form of linear matrix inequalities. Finally, the practicality and effectiveness of this approach were demonstrated through four numerical examples.
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Dates and versions

hal-04445400 , version 1 (07-02-2024)

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Saravanan Shanmugam, R Vadivel, Mohamed Rhaima, Hamza Ghoudi. Improved results on an extended dissipative analysis of neural networks with additive time-varying delays using auxiliary function-based integral inequalities. AIMS Mathematics, 2023, 8, pp.21221 - 21245. ⟨10.3934/math.20231082⟩. ⟨hal-04445400⟩
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