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Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions

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  • Sakthivel, R.
  • Anbuvithya, R.
  • Mathiyalagan, K.
  • Ma, Yong-Ki
  • Prakash, P.

Abstract

This paper is concerned with anti-synchronization results for a class of memristor-based bidirectional associate memory (BAM) neural networks with different memductance functions and time-varying delays. Based on drive-response system concept, differential inclusions theory and Lyapunov stability theory, some sufficient conditions are obtained to guarantee the reliable asymptotic anti-synchronization criterion for memristor-based BAM networks. The memristive BAM neural network is formulated for two types of memductance functions. Sufficient results are derived in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed criterion is demonstrated through numerical example.

Suggested Citation

  • Sakthivel, R. & Anbuvithya, R. & Mathiyalagan, K. & Ma, Yong-Ki & Prakash, P., 2016. "Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 213-228.
    • Handle: RePEc:eee:apmaco:v:275:y:2016:i:c:p:213-228
    DOI: 10.1016/j.amc.2015.11.060
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    References listed on IDEAS

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    12. Lü, Ling & Li, Chengren & Li, Gang & Bai, Suyuan & Gao, Yan & Yan, Zhe & Rong, Tingting, 2018. "Adaptive synchronization of uncertain time-delayed and multi-link network with arbitrary topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 355-365.
    13. Zhang, Ruimei & Zeng, Deqiang & Zhong, Shouming & Yu, Yongbin, 2017. "Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 57-74.
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