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O(t^(-β))-SYNCHRONIZATION AND ASYMPTOTIC SYNCHRONIZATION OF DELAYED FRACTIONAL ORDER NEURAL NETWORKS
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作者 Anbalagan PRATAP Ramachandran RAJA +3 位作者 Jinde CAO chuangxia huang Jehad ALZABUT Ovidiu BAGDASAR 《Acta Mathematica Scientia》 SCIE CSCD 2022年第4期1273-1292,共20页
This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing ... This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing a state feedback control law and a new kind of fractional order Lyapunov functional,a new set of algebraic sufficient conditions are derived to guarantee the O(t^(-β))Synchronization and asymptotic synchronization of the considered FBAMNNs model;this can easily be evaluated without using a MATLAB LMI control toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results. 展开更多
关键词 O(t^(-β))-synchronization asymptotic synchronization BAM neural networks fractional order state feedback control law
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Periodicity of non-autonomous inertial neural networks involving proportional delays and non-reduced order method 被引量:1
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作者 chuangxia huang Hua Zhang 《International Journal of Biomathematics》 SCIE 2019年第2期101-113,共13页
This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-redu... This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-reduced order method is used to establish some novel assertions on the existence and generalized exponential stability of periodic solutions for the addressed model.In addition,an example and its numerical simulations are given to support the proposed approach. 展开更多
关键词 INERTIAL neural networks periodic solution generalized exponential stability proportional delay non-reduced order METHOD
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