Display options
Cite
Nagamani G, Rajan GS, Zhu Q. Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components. IEEE Trans Cybern. 2019;50(10):4281-4292doi: 10.1109/TCYB.2019.2902864.
Nagamani, G., Rajan, G. S., & Zhu, Q. (2020). Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components. IEEE transactions on cybernetics, 50(10), 4281-4292. https://doi.org/10.1109/TCYB.2019.2902864
Nagamani, Gnaneswaran, et al. "Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components." IEEE transactions on cybernetics vol. 50,10 (2020): 4281-4292. doi: https://doi.org/10.1109/TCYB.2019.2902864
Nagamani G, Rajan GS, Zhu Q. Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components. IEEE Trans Cybern. 2020 Oct;50(10):4281-4292. doi: 10.1109/TCYB.2019.2902864. Epub 2019 Mar 20. PMID: 30908249.
Copy Download .nbib Format: NLM
Share it on

IEEE Trans Cybern. 2020 Oct;50(10):4281-4292. doi: 10.1109/TCYB.2019.2902864. Epub 2019 Mar 20.

Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components.

IEEE transactions on cybernetics

Gnaneswaran Nagamani, Ganesan Soundara Rajan, Quanxin Zhu

PMID: 30908249 DOI: 10.1109/TCYB.2019.2902864

Abstract

This paper focuses on the dynamical behavior for a class of memristor-based bidirectional associative memory neural networks (BAMNNs) with additive time-varying delays in discrete-time case. The necessity of the proposed problem is to design a proper state estimator such that the dynamics of the corresponding estimation error is exponentially stable with a prescribed decay rate. By constructing an appropriate Lyapunov-Krasovskii functional (LKF) and utilizing Cauchy-Schwartz-based summation inequality, the delay-dependent sufficient conditions for the existence of the desired estimator are derived in the absence of uncertainties which are further extended to available uncertain parameters of the prescribed memristor-based BAMNNs in terms of linear matrix inequalities (LMIs). By solving the proposed LMI conditions the estimation gain matrices are obtained. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed results.

Publication Types