PERIODICITY AND FIXED-TIME STABILIZATION OF DISCONTINUOUS NEURAL NETWORKS WITH MIXED DELAYS: UNBOUNDED DELAY-DEPENDENT CRITERIA

doi:10.1007/s10473-025-0323-3

Abstract

Abstract: In this paper, a class of discontinuous neutral-type neural networks (NTNNs) with proportional delays is considered. The targets of the paper are to study the problem of periodic solutions and fixed-time (FXT) stabilization of the addressed neural networks. In order to complete the targets, based on set-valued map, differential inclusions theory, coincidence theorem and Hölder inequality technique, some new proportional delay-dependent criteria shown by the inequalities are derived. Based on the fact of the existence of solution, further by applying the FXT stability lemmas and equivalent transformation, the zero solution of closed-loop system achieves FXT stabilization and the corresponding settling-times are estimated. Some previous related works on NTNNs are extended. Finally, one typical example is provided to show the effectiveness of the established results.

Key words: Fixed-time stabilization, Periodic solutions, Discontinuous neural systems, Differential inclusions theory, Proportional delays

CLC Number:

34A36

Ziwei WANG, Lin SUN, Fanchao KONG, Rathinasamy SAKTHIVEL. PERIODICITY AND FIXED-TIME STABILIZATION OF DISCONTINUOUS NEURAL NETWORKS WITH MIXED DELAYS: UNBOUNDED DELAY-DEPENDENT CRITERIA[J].Acta mathematica scientia,Series B, 2025, 45(3): 1188-1204.

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