Design disturbance attenuating controller for memristive recurrent neural networks with mixed time-varying delays View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Jianying Xiao, Shouming Zhong, Fang Xu

ABSTRACT

This paper investigates the design of disturbance attenuating controller for memristive recurrent neural networks (MRNNs) with mixed time-varying delays. By applying the combination of differential inclusions, set-valued maps and Lyapunov–Razumikhin, a feedback control law is obtained in the simple form of linear matrix inequality (LMI) to ensure disturbance attenuation of memristor-based neural networks. Finally, a numerical example is given to show the effectiveness of the proposed criteria. More... »

PAGES

189

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13662-018-1641-8

DOI

http://dx.doi.org/10.1186/s13662-018-1641-8

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https://app.dimensions.ai/details/publication/pub.1104123778


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55 schema:description This paper investigates the design of disturbance attenuating controller for memristive recurrent neural networks (MRNNs) with mixed time-varying delays. By applying the combination of differential inclusions, set-valued maps and Lyapunov–Razumikhin, a feedback control law is obtained in the simple form of linear matrix inequality (LMI) to ensure disturbance attenuation of memristor-based neural networks. Finally, a numerical example is given to show the effectiveness of the proposed criteria.
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