Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components

Nagamani, G.; Soundararajan, G.; Zhu, Quanxin

doi:10.1109/tcyb.2019.2902864

Cited by 25 publications

(8 citation statements)

References 30 publications

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“…the discontinuous function is more general. Furthermore, the results in the previous studies [40][41][42] are not applicable for the system investigated in this work. Then, this article generalizes all the results in the previous studies.…”

Section: Theorem 3 Under H(1)-h(4) If the Following Conditions (22) Hold And There Exist Positive Constantsmentioning

confidence: 81%

“…where i = 1, 2 and the parameters are chosen to be: By simple calculation, it is easy to obtain that these control gains satisfy the conditions (22). Moreover, we choose 𝛼 = 0.5, (𝜁 4 1 ) 0 = (𝜁 4 1 ) 1 = (𝜁 4 1 ) 2 = (𝜁 4 1 ) 12 = 9.5 and (𝜁 By Theorem 2, systems (40) and ( 41) are finite-timely synchronized (see Figures 1 and 2). From the inequality (33), we can calculate the settling time of synchronization T0 ≤ 0.8763.…”

Section: Examplementioning

confidence: 99%

“…Consider drive-response systems (40) and (41) under the controller (34) in Theorem 3. All the parameters in the models (40) and ( 41) are defined in Section 5.1.…”

Section: Examplementioning

confidence: 99%

“…Consider drive-response systems (40) and (41) under the controller (34) in Theorem 3. All the parameters in the models (40) and ( 41) are defined in Section 5.1. According to the conditions (22) in Theorem 3, the parameters of controller (34) are given as follows: Remark 13.…”

Section: Examplementioning

confidence: 99%

“…Nonetheless, the systematic coefficients of this article are allowed to be time varying. On the other hand, the neural networks models (40) and (41) in Sections 5.1 and 5.2 include time delays and discontinuous of neuron activations, by combining them, the advantage of our method concerning finite-time and fixed-time synchronization of FCVCGNNs are shown.…”

Section: Data Availability Statementmentioning

confidence: 99%

See 4 more Smart Citations

Finite‐time and fixed‐time synchronization of fuzzy Clifford‐valued Cohen‐Grossberg neural networks with discontinuous activations and time‐varying delays

Aouiti

Bessifi

2021

Adaptive Control & Signal

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In this article, we are concerned with fuzzy Clifford-valued Cohen-Grossberg neural networks (FCVCGNNs) via discontinuous activations and time-varying delays. First, the time-delayed feedback strategy is used to investigate the synchronization in finite-time and fixed-time of FCVCGNNs with discontinuous activations and time-varying delays. By designing Lyapunov functions and utilizing differential inequalities, several effective conditions are derived to ensure synchronization in finite-time and fixed-time of the addressed neural networks. A novel fixed-time convergence method is proposed to study synchronization in fixed-time of discontinuous delayed FCVCGNNs. Furthermore, the settling time of synchronization are estimated. In the end, two numerical examples with simulations are given to confirm the effectiveness of the synchronization criteria.

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Section: Theorem 3 Under H(1)-h(4) If the Following Conditions (22) Hold And There Exist Positive Constantsmentioning

confidence: 81%

Section: Examplementioning

confidence: 99%

“…Consider drive-response systems (40) and (41) under the controller (34) in Theorem 3. All the parameters in the models (40) and ( 41) are defined in Section 5.1.…”

Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

See 3 more Smart Citations

Finite‐time and fixed‐time synchronization of fuzzy Clifford‐valued Cohen‐Grossberg neural networks with discontinuous activations and time‐varying delays

Aouiti

Bessifi

2021

Adaptive Control & Signal

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Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

Adhira

Nagamani

2023

Asian Journal of Control

Self Cite

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This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach.

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A new method for global h$$ h $$‐stability analysis for discrete‐time nonlinear systems with time‐varying delays

Zhang,

Yang

et al. 2024

Math Methods in App Sciences

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In this paper, we characterize the global ‐stability of discrete‐time nonlinear systems (DTNSs) with time‐varying delays. The global ‐stability encompasses several well‐known types of stability by using different choices of functions, such as global exponential stability (ES), Lagrange exponential stability (LES), and asymptotic stability. By exploiting a direct method based on the system solutions, we give a sufficient condition for the global ‐stability of the DTNSs in the form of simple inequalities that can be easily handled in MATLAB. Finally, numerical examples are presented to demonstrate the applicability of the method.

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Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components

Cited by 25 publications

References 30 publications

Finite‐time and fixed‐time synchronization of fuzzy Clifford‐valued Cohen‐Grossberg neural networks with discontinuous activations and time‐varying delays

Finite‐time and fixed‐time synchronization of fuzzy Clifford‐valued Cohen‐Grossberg neural networks with discontinuous activations and time‐varying delays

Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

A new method for global h$$ h $$‐stability analysis for discrete‐time nonlinear systems with time‐varying delays

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