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<p>This paper investigates the finite-time dissipativity problem for uncertain tempered fractional-order neural networks with constant time delays and external disturbances. By employing a novel analytical framework combining the inf-sup technique, Laplace transform for tempered Caputo derivatives, generalized Grönwall inequality, and linear matrix inequality (LMI) conditions, new tractable and less conservative finite-time dissipativity criteria are derived. The proposed approach ensures both transient state boundedness and input-output energy dissipation performance over finite time horizons, offering stronger performance guarantees than classical finite-time stability. Numerical simulations are presented to validate the theoretical results.</p>

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Finite-time dissipativity analysis of uncertain tempered fractional-order neural networks with delays via LMI approach

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Tran Ngoc Tuan,
  • Nguyen Truong Thanh,
  • Mai Viet Thuan,
  • Nguyen Thi Thanh Huyen

摘要

This paper investigates the finite-time dissipativity problem for uncertain tempered fractional-order neural networks with constant time delays and external disturbances. By employing a novel analytical framework combining the inf-sup technique, Laplace transform for tempered Caputo derivatives, generalized Grönwall inequality, and linear matrix inequality (LMI) conditions, new tractable and less conservative finite-time dissipativity criteria are derived. The proposed approach ensures both transient state boundedness and input-output energy dissipation performance over finite time horizons, offering stronger performance guarantees than classical finite-time stability. Numerical simulations are presented to validate the theoretical results.