Finite-time synchronization of fractional-order fuzzy cellular neural networks with mixed delays and interactions via quantized memory sampled-data control

Synchronization Analysis of Fractional-Order Competitive Neural Networks with Additive Time Varying Delay via Quantized Sampled Data Control and Its Application to Image Encryption

Exponential synchronization of fractional-order complex-valued BAM neural networks with time-varying delays via event-triggered control for image encryption

Frequency-Domain Asymptotic Synchronization Criteria for Delayed Fuzzy BAM Neural Networks Under Novel Controllers

This paper studies the asymptotic synchronization problem for drive-response delayed fuzzy bidirectional associative memory (BAM) neural networks via a frequency-domain approach. The network model explicitly retains fuzzy logic operations—fuzzy AND and fuzzy OR—in its connection weights, as originally proposed in the l...

Sampled-data exponential stabilization for delayed complex valued neural networks with quantized intermittent communication

This paper addresses the sampled-data exponential stabilization problem for delayed complex-valued neural networks (DCNNs) over capacity-limited communication channels. To reduce communication load, a quantized intermittent sampled-data transmission (QIST) protocol is proposed. A transmission-interval-dependent Lyapuno...

Finite-time synchronization of neuronal networks based on double-power sliding mode control

The finite-time convergence of both the reaching phase and sliding phase of the control system is strictly proved, and the explicit upper bound of network synchronization time is deduced, which quantitatively reveals the correlation between synchronization efficiency and system initial states as well as control paramet...

Event-Triggered Quantized Synchronization via Sampled-Data Iterative Learning Control for Coupled Fractional-Order Time-Delayed Competitive Neural Networks

This paper examines the problem of achieving synchronization within fractional-order competitive neural networks (FOCNNs) affected by intrinsic transmission delays. To overcome the limitations associated with restricted network bandwidth and excessive computational expenses, a quantized sampled-data distributed iterati...