Search

• A class of neurodynamic model with fuzziness, inertial terms, discontinuous feedback functions and distributed delays is proposed. • The fixed-time synchronization and predefined-time synchronization results for proposed delayed drive-response inertial neural networks are established directly without variables substitution method. • Two new effective feedback controllers were, respectively, designed to get new criteria on fixed-time synchronization and predefined-time synchronization of drive-response inertial neural networks with distributed delays. • The predefined-time synchronization criteria for the proposed neurodynamic model are established in this paper, which convergence rate is more faster and settling time do not depend on initial values of drive response systems. This paper focuses on the fixed/predefined-time synchronization control of drive-response fuzzy inertial discontinuous neural networks(FIDNNs) with distributed delays. By using non-smooth analysis, fixed/predefined-time stability lemmas and designing two new fixed/predefined-time feedback controllers, several novel results are given to get fixed-time synchronization(FTS) and predefined-time synchronization(PTS) for the proposed FIDNNs. The fixed/predefined-time results of this paper are derived directly from FIDNNs themselves without variables substitution method. Up until now, very few results are reported on non-reduced order approach to achieve FTS and PTS of FIDNNs with distributed delays. Finally, some simulations and applications are, respectively, carried out to expound the efficacy of the obtained new FTS and PTS criteria. [ABSTRACT FROM AUTHOR]

• In this paper, the p-norm fixed-time synchronization of delayed ICVNNs is investigated for the first time, in which the settling time estimation in terms of p-norm is derived. Our results are more flexible and adjustable than these results [36–38]. • Compared with the works [3,13,36-38,42], what we get herein is fixed-time synchronization, and these results are also applied to image encryption. • Different from the real-valued INNs [5–7,19,21] and first-order CVNNs [13,39-42]. In this paper, the neural model is defined in the complex field and has an inertial term, which broadens the application range of INNs and makes up for some previous literature results. This paper researches the problem of p-norm fixed-time synchronization for a class of delayed inertial complex-valued neural networks (ICVNNs). By using reduced-order transformation and separating real and imaginary parts of complex-valued parameters , the second-order ICVNNs can be converted into the form of first-order real-valued differential equations. Then some new flexible and adjustable algebraic criteria to ensure the fixed-time synchronization of ICVNNs are established by means of the non-smooth Lyapunov function and inequality analytical techniques. Moreover, the settling time of fixed-time synchronization is theoretically estimated, which does not depend on the initial value of systems. Finally, simulation examples and applications are presented to illustrate the validity and availability of the obtained results. [ABSTRACT FROM AUTHOR]