Exponential input-to-state stability for complex-valued memristor-based BAM neural networks with multiple time-varying delays

Runan Guo,Xiaoping Liu,Haixia Wang,Ziye Zhang,Chong Lin,Jian Chen

doi:10.1016/j.neucom.2017.10.038

Exponential input-to-state stability for complex-valued memristor-based BAM neural networks with multiple time-varying delays

Runan Guo, Xiaoping Liu + Show 4 more

https://doi.org/10.1016/j.neucom.2017.10.038

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Journal: Neurocomputing	Publication Date: Nov 7, 2017
Citations: 37

Affiliation: Shandong University of Science and Technology, Lakehead University, Qingdao University, Qingdao University of Technology

#Bidirectional Associative Memory Neural Networks #Multiple Time-varying Delays + Show 8 more

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Abstract

Abstract In this paper, the exponential input-to-state stability (ISS) for complex-valued memristor-based bidirectional associative memory (BAM) neural networks with multiple time-varying delays is discussed. By constructing a novel Lyapunov functional and utilizing inequality techniques, a sufficient criterion of the exponential input-to-state stability for the considered system is firstly derived. Moreover, similar result is also obtained for delayed complex-valued BAM neural networks without memristors. Finally, two numerical examples are given to demonstrate the effectiveness of the obtained results.

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