Journal ArticleDOI
Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays
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TLDR
This paper investigates the global asymptotic stability and stabilization of memristive neural networks (MNNs) with communication delays via event-triggered sampling control through a newly augmented Lyapunov-Krasovskii functional.About:
This article is published in Applied Mathematics and Computation.The article was published on 2017-10-01. It has received 178 citations till now. The article focuses on the topics: Control theory & Exponential stability.read more
Citations
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Journal ArticleDOI
Non-fragile memory filtering of T-S fuzzy delayed neural networks based on switched fuzzy sampled-data control
TL;DR: A modified loose-looped fuzzy membership functions (FMFs) dependent Lyapunov-Krasovskii functional (LKF) is constructed based on the information of the time derivative of FMFs, which involves not only a signal transmission delay but also switched topologies.
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Adaptive Event-Triggered Fault Detection Scheme for Semi-Markovian Jump Systems With Output Quantization
TL;DR: An adaptive event-triggered scheme for S-MJSs that is more effective than conventional event- triggered strategy for decreasing network transmission information is developed and a new adaptive law is designed that can dynamically adjust the event-Triggered threshold is designed.
Journal ArticleDOI
Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout
TL;DR: A novel stochastic switched sampled-data controller with time-varying sampling is developed in the frame of the zero-input strategy and novel synchronization criteria are established to guarantee that DCNNs are synchronous exponentially when the control packet dropout occurs in a random way.
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Fixed-time synchronization of inertial memristor-based neural networks with discrete delay
TL;DR: Four different kinds of feedback controllers are designed, under which the considered inertial memristor-based neural networks can realize fixed-time synchronization perfectly and the obtained fixed- time synchronization criteria can be verified by algebraic operations.
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Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays
TL;DR: This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control and a novel Lyapunov–Krasovskii functional is constructed for synchronizing an error system.
References
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Journal ArticleDOI
Reliable stabilization for memristor-based recurrent neural networks with time-varying delays
Kalidass Mathiyalagan,Ranganathan Anbuvithya,Rathinasamy Sakthivel,Ju H. Park,Periasamy Prakash +4 more
TL;DR: A model of memristive based RNNs based on a suitable Lyapunov–Krasovskii functional and using linear matrix inequality framework, sufficient conditions are presented for the existence of a reliable state feedback controller, which can guarantee the global asymptotic stability of the memristives.
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Event-triggered networked H∞ control of discrete-time nonlinear singular systems
TL;DR: A delay-distribution-dependent criterion is derived which guarantees the closed-loop networked discrete-time nonlinear singular system is regular, causal, and stable with a certain H∞ performance index.
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Global synchronization of stochastically disturbed memristive neurodynamics via discontinuous control laws
TL;DR: The theoretical results on the master-slave synchronization of two memristive neural networks in the presence of additive noise are presented and an adaptive control law consisting of a linear feedback term and a discontinuous feedback term is designed to achieve global synchronization in mean square.
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Improved conditions for global exponential stability of a general class of memristive neural networks
TL;DR: The proposed framework for theoretical analysis of memristive neurodynamic systems may be useful in developing nanoscale memristor device as synapse in neuromorphic computing architectures.