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New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays

Jun Wang, Yongqiang Tian, Lanfeng Hua, Kaibo Shi, Shouming Zhong, Shiping Wen

2023Mathematics28 citationsDOIOpen Access PDF

Abstract

In this work, we are concerned with the finite-time synchronization (FTS) control issue of the drive and response delayed memristor-based inertial neural networks (MINNs). Firstly, a novel finite-time stability lemma is developed, which is different from the existing finite-time stability criteria and extends the previous results. Secondly, by constructing an appropriate Lyapunov function, designing effective delay-dependent feedback controllers and combining the finite-time control theory with a new non-reduced order method (NROD), several novel theoretical criteria to ensure the FTS for the studied MINNs are provided. In addition, the obtained theoretical results are established in a more general framework than the previous works and widen the application scope. Lastly, we illustrate the practicality and validity of the theoretical results via some numerical examples.

Topics & Concepts

MemristorControl theory (sociology)Computer scienceSynchronization (alternating current)Lemma (botany)Artificial neural networkInertial frame of referenceChaoticStability (learning theory)Discrete time and continuous timeFunction (biology)Lyapunov functionControl (management)MathematicsArtificial intelligenceEngineeringNonlinear systemChannel (broadcasting)Machine learningElectronic engineeringPoaceaeStatisticsPhysicsEvolutionary biologyComputer networkEcologyQuantum mechanicsBiologyNeural Networks Stability and SynchronizationAdvanced Memory and Neural Computingstochastic dynamics and bifurcation
New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays | Litcius