A General Approach to Fixed-time Synchronization Problem for Fractional-order Multi-dimension-valued Fuzzy Neural Networks Based on Memristor

2021 ◽  
pp. 1-1
Author(s):  
Jianying Xiao ◽  
Jun Cheng ◽  
Kaibo Shi ◽  
Ruimei Zhang
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Time Synchronization ◽  
Fixed Time ◽  
Fuzzy Neural Networks ◽  
Synchronization Problem ◽  
Fuzzy Neural

scholarly journals Synchronization of Chaotic Delayed Fuzzy Neural Networks under Impulsive and Stochastic Perturbations

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Li ◽  
Qiankun Song
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
Fuzzy Neural Networks ◽  
Itô’S Formula ◽  
Mixed Delays ◽  
Fuzzy Cellular Neural Networks ◽  
Stochastic Perturbations ◽  
Synchronization Problem ◽  
Ito’S Formula ◽  
Fuzzy Neural

The synchronization problem of chaotic fuzzy cellular neural networks with mixed delays is investigated. By an impulsive integrodifferential inequality and the Itô's formula, some sufficient criteria to synchronize the networks under both impulsive and stochastic perturbations are obtained. The example and simulations are given to demonstrate the efficiency and advantages of the proposed results.

scholarly journals Improved Results on Finite-Time Passivity and Synchronization Problem for Fractional-Order Memristor-Based Competitive Neural Networks: Interval Matrix Approach

2022 ◽  
Vol 6 (1) ◽  
pp. 36
Author(s):  
Pratap Anbalagan ◽  
Raja Ramachandran ◽  
Jehad Alzabut ◽  
Evren Hincal ◽  
Michal Niezabitowski
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Finite Time ◽  
Time Synchronization ◽  
Initial Conditions ◽  
Interval Matrix ◽  
Linear Matrix ◽  
Parameter Mismatch ◽  
Synchronization Problem ◽  
State Dependent

This research paper deals with the passivity and synchronization problem of fractional-order memristor-based competitive neural networks (FOMBCNNs) for the first time. Since the FOMBCNNs’ parameters are state-dependent, FOMBCNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, the conventional robust control scheme cannot guarantee the synchronization of FOMBCNNs. Under the framework of the Filippov solution, the drive and response FOMBCNNs are first transformed into systems with interval parameters. Then, the new sufficient criteria are obtained by linear matrix inequalities (LMIs) to ensure the passivity in finite-time criteria for FOMBCNNs with mismatched switching jumps. Further, a feedback control law is designed to ensure the finite-time synchronization of FOMBCNNs. Finally, three numerical cases are given to illustrate the usefulness of our passivity and synchronization results.

scholarly journals The Fast Fixed-Time Bipartite Synchronization of Coupled Delayed Neural Networks with Signed Graphs

2021 ◽  
Author(s):  
Shiju Yang ◽  
Chuandong Li ◽  
Yu Li ◽  
Ting Yang ◽  
Bo Li
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Lyapunov Stability Theory ◽  
Fixed Time ◽  
Initial Condition ◽  
Signed Graphs ◽  
Comparison System ◽  
Delayed Neural Networks ◽  
Synchronization Problem ◽  
System Method

Abstract In this paper, the fixed-time bipartite synchronization problem for coupled delayed neural networks with signed graphs is discussed. Different from traditional neural networks, the interactions between nodes of delayed neural networks can be either collaborative or antagonistic. Furthermore, compared with the initial-condition based finite-time synchronization, the settling time is bounded by a constant within fixedtime regardless of the initial condition. It is worth noting that the fixed-time stable network for bipartite synchronization in this paper achieves more faster convergence than most existing publications. By applying constructing comparison system method, Lyapunov stability theory and inequality techniques, some sufficient criteria for fixed-time bipartite synchronization are obtained. Finally, two numerical examples are granted to display the performance of the obtained results.

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