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Weighted sum synchronization of memristive coupled neural networks
- Source :
- Neurocomputing. 403:211-223
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- It is well known that weighted sum of node states plays an essential role in function implementation of neural networks. Therefore, this paper proposes a new weighted sum synchronization model for memristive neural networks. Unlike the existing synchronization models of memristive neural networks which control each network node to reach synchronization, the proposed model treats the networks as dynamic entireties by weighted sum of node states and makes the entireties instead of each node reach expected synchronization. In this paper, weighted sum complete synchronization and quasi-synchronization are both investigated by designing feedback controller and aperiodically intermittent controller, respectively. Meanwhile, a flexible control scheme is designed for the proposed model by utilizing some switching parameters and can improve anti-interference ability of control system. By applying Lyapunov method and some differential inequalities, some effective criteria are derived to ensure the synchronizations of memristive neural networks. Moreover, the error level of the quasi-synchronization is given. Finally, numerical simulation examples are used to certify the effectiveness of the derived results.
- Subjects :
- Lyapunov function
0209 industrial biotechnology
Artificial neural network
Computer science
Cognitive Neuroscience
Node (networking)
Intermittent control
02 engineering and technology
Function (mathematics)
Topology
Synchronization
Computer Science Applications
symbols.namesake
020901 industrial engineering & automation
Artificial Intelligence
Control theory
Control system
Synchronization (computer science)
0202 electrical engineering, electronic engineering, information engineering
symbols
020201 artificial intelligence & image processing
Differential inequalities
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 403
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........08b5887aab6fcec0b53f4c0eebf7e453