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Stability analysis of memristive multidirectional associative memory neural networks and applications in information storage

Weiping Wang

http://orcid.org/0000-0002-6796-7596

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

Institute of Physics, Humboldt-University, Berlin 10099, Germany

Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany

E-mail Address: [email protected]

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Xin Yu

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

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Xiong Luo

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

E-mail Address: [email protected]

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and

Lixiang Li

Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

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https://doi.org/10.1142/S021798491850207XCited by:6

Abstract

Traditional biological neural networks lack the capability of reflecting variable synaptic weights when simulating associative memory of human brains. In this paper, we investigate the existence and exponential stability of a novel memristive multidirectional associative memory neural networks (MAMNNs) model, which includes the time-varying delays. In the proposed approach, the time-varying delays are set to be bounded, and it is not necessary for their derivative to be differentiable. With removal of certain conditions, less conservative results are generated. Sufficient criteria guaranteeing the stability of the memristive MAMNNs are derived based on the Lyapunov function and some inequality techniques. To illustrate the performance of the proposed criteria, a procedure is designed to realize information storage. Meanwhile, the effectiveness of the proposed theories is validated with numerical experiments.

Keywords:

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Published: 11 June 2018

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Vol. 32, No. 18

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Received 20 February 2018

Revised 11 April 2018

Accepted 17 April 2018

Keywords

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Stability analysis of memristive multidirectional associative memory neural networks and applications in information storage

Abstract

References

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