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Local Synchronization of Interconnected Boolean Networks With Stochastic Disturbances
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 31:452-463
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- This paper is concerned with the local synchronization problem for the interconnected Boolean networks (BNs) without and with stochastic disturbances. For the case without stochastic disturbances, first, the limit set and the transient period of the interconnected BNs are discussed by resorting to the properties of the reachable set for the global initial states set. Second, in terms of logical submatrices of a certain Boolean vector, a compact algebraic expression is presented for the limit set of the given initial states set. Based on it, several necessary and sufficient conditions are derived assuring the local synchronization of the interconnected BNs. Subsequently, an efficient algorithm is developed to calculate the largest domain of attraction. As for the interconnected BNs with stochastic disturbances, first, mutually independent two-valued random logical variables are introduced to describe the stochastic disturbances. Then, the corresponding local synchronization criteria are also established, and the algorithm to calculate the largest domain of attraction is designed. Finally, numerical examples are employed to illustrate the effectiveness of the obtained results/ algorithms.
- Subjects :
- Computer Networks and Communications
Computer science
Block matrix
02 engineering and technology
Topology
Domain (mathematical analysis)
Computer Science Applications
Set (abstract data type)
Artificial Intelligence
Synchronization (computer science)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Transient (computer programming)
Algebraic expression
Limit set
Software
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....22f8c4b5cc451c7147046edecd0f6104