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Wang JA, Wen XY, Hou BY. Advanced stability criteria for static neural networks with interval time-varying delays via the improved Jensen inequality. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Fractional delay segments method on time-delayed recurrent neural networks with impulsive and stochastic effects: An exponential stability approach. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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3
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Lyapunov Functions to Caputo Fractional Neural Networks with Time-Varying Delays. AXIOMS 2018. [DOI: 10.3390/axioms7020030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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4
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Chen D, Zhang Y, Li S. Zeroing neural-dynamics approach and its robust and rapid solution for parallel robot manipulators against superposition of multiple disturbances. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.09.032] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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5
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Ma Y, Ma N, Chen L, Zheng Y, Han Y. Exponential stability for the neutral-type singular neural network with time-varying delays. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0764-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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6
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Delay-dependent dissipativity of neural networks with mixed non-differentiable interval delays. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.04.059] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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7
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Jiang M, Mu J, Huang D. Globally exponential stability and dissipativity for nonautonomous neural networks with mixed time-varying delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.04.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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New delay-interval-dependent stability criteria for static neural networks with time-varying delays. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.12.063] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Wang X, She K, Zhong S, Yang H. New and improved results for recurrent neural networks with interval time-varying delay. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.10.086] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Yang B, Wang R, Dimirovski GM. Delay-dependent stability for neural networks with time-varying delays via a novel partitioning method. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.08.058] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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Jiang Y, Li C. Exponential stability of memristor-based synchronous switching neural networks with time delays. INT J BIOMATH 2015. [DOI: 10.1142/s1793524516500169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we study the existence, uniqueness and stability of memristor-based synchronous switching neural networks with time delays. Several criteria of exponential stability are given by introducing multiple Lyapunov functions. In comparison with the existing publications on simplice memristive neural networks or switching neural networks, we consider a system with a series of switchings, these switchings are assumed to be synchronous with memristive switching mechanism. Moreover, the proposed stability conditions are straightforward and convenient and can reflect the impact of time delay on the stability. Two examples are also presented to illustrate the effectiveness of the theoretical results.
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Affiliation(s)
- Yinlu Jiang
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, P. R. China
| | - Chuandong Li
- College of Electronic and Information Engineering, Southwest University, Chongqing 400715, P. R. China
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Zhang G, Shen Y, Xu C. Global exponential stability in a Lagrange sense for memristive recurrent neural networks with time-varying delays. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.08.064] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Xiao N, Jia Y. New approaches on stability criteria for neural networks with two additive time-varying delay components. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.02.028] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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15
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New passivity conditions with fewer slack variables for uncertain neural networks with mixed delays. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.02.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Zhu S, Shen Y. Robustness analysis for connection weight matrices of global exponential stable time varying delayed recurrent neural networks. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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18
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Robustness analysis for connection weight matrix of global exponential stability recurrent neural networks. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.08.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Liu Y, Ma W, Mahmoud MS. New results for global exponential stability of neural networks with varying delays. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.05.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Tian J, Zhong S. Improved delay-dependent stability criteria for neural networks with two additive time-varying delay components. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.08.027] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Tian J, Zhong S. New delay-dependent exponential stability criteria for neural networks with discrete and distributed time-varying delays. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.05.024] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zhu S, Shen Y. Passivity analysis of stochastic delayed neural networks with Markovian switching. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.02.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Fu J, Zhang H, Ma T, Zhang Q. On passivity analysis for stochastic neural networks with interval time-varying delay. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2009.10.010] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Li T, Ye X. Improved stability criteria of neural networks with time-varying delays: An augmented LKF approach. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2009.10.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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27
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Balasubramaniam P, Rakkiyappan R. Delay-dependent robust stability analysis of uncertain stochastic neural networks with discrete interval and distributed time-varying delays. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Zheng CD, Lu LB, Wang ZS. New LMT-based delay-dependent criterion for global asymptotic stability of cellular neural networks. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2009.01.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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29
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Zheng CD, Zhang H, Wang Z. Novel delay-dependent criteria for global robust exponential stability of delayed cellular neural networks with norm-bounded uncertainties. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2008.08.017] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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