Event-triggered passivity and synchronization of delayed multiple-weighted coupled reaction–diffusion neural networks with non-identical nodes

Lin, Shanrong; Huang, Yanli; Ren, Shun-Yan

doi:10.1016/j.neunet.2019.08.031

Cited by 40 publications

(37 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, multi-weighted CNNs (MWCNNs) have become a very hot topic due to their broad application foreground. Some standards were established to ensure passivity and synchronization for MWCNNs [48][49][50]. Tang et al [49] investigated the passivity and exponential synchronization for two classes of MWCNNs with and without time delays.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Wang

Huang

Yang

2020

IET Control Theory & Applications

Self Cite

View full text Add to dashboard Cite

A multi-weighted coupled neural networks (MWCNNs) model with event-triggered communication is studied in this paper. On the one hand, the passivity of the presented network model is studied by utilizing Lyapunov stability theory and some inequality techniques, and a synchronization criterion based on the obtained output-strict passivity condition of MWCNNs with event-triggered communication is derived. On the other hand, some robust passivity and robust synchronization criteria based on output-strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output-strict passivity and robust synchronization results.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Moreover, multi‐weighted CNNs (MWCNNs) have become a very hot topic due to their broad application foreground. Some standards were established to ensure passivity and synchronisation for MWCNNs [48–50]. Tang et al .…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Wang

Huang

Yang

2020

IET Control Theory & Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…To reduce the unnecessary data transmission, the event trigger mechanism is proposed [28], in which the data packets are only transmitted when the corresponding conditions are satisfied. Recently, much effort has been made for the event‐triggered condition design [29–32]. Based on the above discussion, we would like to consider the dissipativity of Markovian MWCNNs with event‐triggered pinning control.…”

Section: Introductionmentioning

confidence: 99%

Dissipativity of Markovian multiple‐weighted coupled neural networks with dynamic event‐triggered pinning control

Chen

Zheng

2020

IET Control Theory & Appl

View full text Add to dashboard Cite

“…Consequently, it is significant to study CNNs composed of nonidentical nodes. To our knowledge, a few investigators have discussed CNs consisting of the same dimensional nonidentical nodes in recent years [37][38][39][40][41]. However, the networks with nonidentical nodes of different dimensions can describe more practical networks.…”

Section: Passivity and Synchronization Of Cdddrdnnsmentioning

confidence: 99%

“…Unfortunately, this case is very rare in the real-world networks. Consequently, the CNs consisting of nonidentical nodes in the same dimension [37][38][39][40][41] have been discussed firstly by researchers. Zhao et al [39] dealt with the synchronization problem of CNs with nonidentical nodes.…”

Section: Introductionmentioning

confidence: 99%

Passivity and Synchronization of Coupled Different Dimensional Delayed Reaction-Diffusion Neural Networks with Dirichlet Boundary Conditions

2020

Self Cite

View full text Add to dashboard Cite

Two types of coupled different dimensional delayed reaction-diffusion neural network (CDDDRDNN) models without and with parametric uncertainties are analyzed in this paper. On the one hand, passivity and synchronization of the raised network model with certain parameters are studied through exploiting some inequality techniques and Lyapunov stability theory, and some adequate conditions are established. On the other hand, the problems of robust passivity and robust synchronization of CDDDRDNNs with parameter uncertainties are solved. Finally, two numerical examples are given to testify the effectiveness of the derived passivity and synchronization conditions.

show abstract

Event-triggered passivity and synchronization of delayed multiple-weighted coupled reaction–diffusion neural networks with non-identical nodes

Cited by 40 publications

References 49 publications

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Event‐triggered communication for passivity and synchronisation of multi‐weighted coupled neural networks with and without parameter uncertainties

Dissipativity of Markovian multiple‐weighted coupled neural networks with dynamic event‐triggered pinning control

Passivity and Synchronization of Coupled Different Dimensional Delayed Reaction-Diffusion Neural Networks with Dirichlet Boundary Conditions

Contact Info

Product

Resources

About