IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v311y2017icp100-117.html
   My bibliography  Save this article

Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays

Author

Listed:
  • Guo, Runan
  • Zhang, Ziye
  • Liu, Xiaoping
  • Lin, Chong

Abstract

This article explores the exponential stability problem of complex-valued bidirectional associative memory (BAM) neural networks with time delays. This analysis is on the basis of the M-matrix approach, the differential inclusions theory and the homeomorphism property. By constructing a novel Lyapunov functional, a sufficient criterion for the existence, uniqueness, and exponential stability for the equilibrium point of the considered system is derived. Moreover, similar results in terms of M-matrix are also obtained for the exponential stability problem of delayed complex-valued BAM neural networks without memristors. In the end, two numerical examples are provided to demonstrate the availability of the obtained results.

Suggested Citation

  • Guo, Runan & Zhang, Ziye & Liu, Xiaoping & Lin, Chong, 2017. "Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 100-117.
    • Handle: RePEc:eee:apmaco:v:311:y:2017:i:c:p:100-117
    DOI: 10.1016/j.amc.2017.05.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300317303090
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2017.05.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James M. Tour & Tao He, 2008. "The fourth element," Nature, Nature, vol. 453(7191), pages 42-43, May.
    2. Goh, S.L. & Chen, M. & Popović, D.H. & Aihara, K. & Obradovic, D. & Mandic, D.P., 2006. "Complex-valued forecasting of wind profile," Renewable Energy, Elsevier, vol. 31(11), pages 1733-1750.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aadhithiyan, S. & Raja, R. & Dianavinnarasi, J. & Alzabut, J. & Baleanu, D., 2023. "Robust synchronization of multi-weighted fractional order complex dynamical networks under nonlinear coupling via non-fragile control with leakage and constant delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Li, Li & Wang, Zhen & Li, Yuxia & Shen, Hao & Lu, Junwei, 2018. "Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays," Applied Mathematics and Computation, Elsevier, vol. 330(C), pages 152-169.
    3. Chang, Wenting & Zhu, Song & Li, Jinyu & Sun, Kaili, 2018. "Global Mittag–Leffler stabilization of fractional-order complex-valued memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 346-362.
    4. Ziye Zhang & Xiaoping Liu & Chong Lin & Bing Chen, 2018. "Finite-Time Synchronization for Complex-Valued Recurrent Neural Networks with Time Delays," Complexity, Hindawi, vol. 2018, pages 1-14, December.
    5. Long, Shaohua & Wu, Yunlong & Zhong, Shouming & Zhang, Dian, 2018. "Stability analysis for a class of neutral type singular systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 113-131.
    6. Xie, Wenqian & Zhu, Hong & Zhong, Shouming & Zhang, Dian & Shi, Kaibo & Cheng, Jun, 2018. "Extended dissipative estimator design for uncertain switched delayed neural networks via a novel triple integral inequality," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 82-102.
    7. Liu, Jin & Jian, Jigui & Wang, Baoxian, 2020. "Stability analysis for BAM quaternion-valued inertial neural networks with time delay via nonlinear measure approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 174(C), pages 134-152.
    8. Wang, Chen & Zhang, Hai & Ye, Renyu & Zhang, Weiwei & Zhang, Hongmei, 2023. "Finite time passivity analysis for Caputo fractional BAM reaction–diffusion delayed neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 424-443.
    9. Jinlong Shu & Lianglin Xiong & Tao Wu & Zixin Liu, 2019. "Stability Analysis of Quaternion-Valued Neutral-Type Neural Networks with Time-Varying Delay," Mathematics, MDPI, vol. 7(1), pages 1-23, January.
    10. Zhang, Dian & Cheng, Jun & Ki Ahn, Choon & Ni, Hongjie, 2019. "A flexible terminal approach to stochastic stability and stabilization of continuous-time semi-Markovian jump systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 342(C), pages 191-205.
    11. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Stochastic Memristive Quaternion-Valued Neural Networks with Time Delays: An Analysis on Mean Square Exponential Input-to-State Stability," Mathematics, MDPI, vol. 8(5), pages 1-26, May.
    12. Shi, Yanchao & Cao, Jinde & Chen, Guanrong, 2017. "Exponential stability of complex-valued memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 222-234.
    13. Grienggrai Rajchakit & Pharunyou Chanthorn & Pramet Kaewmesri & Ramalingam Sriraman & Chee Peng Lim, 2020. "Global Mittag–Leffler Stability and Stabilization Analysis of Fractional-Order Quaternion-Valued Memristive Neural Networks," Mathematics, MDPI, vol. 8(3), pages 1-29, March.
    14. Shan, Yaonan & She, Kun & Zhong, Shouming & Zhong, Qishui & Shi, Kaibo & Zhao, Can, 2018. "Exponential stability and extended dissipativity criteria for generalized discrete-time neural networks with additive time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 145-168.
    15. Wei, Xiaofeng & Zhang, Ziye & Lin, Chong & Chen, Jian, 2021. "Synchronization and anti-synchronization for complex-valued inertial neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    16. Jie Pan & Lianglin Xiong, 2021. "Novel Criteria of Stability for Delayed Memristive Quaternionic Neural Networks: Directly Quaternionic Method," Mathematics, MDPI, vol. 9(11), pages 1-14, June.
    17. Zhen Yang & Zhengqiu Zhang, 2022. "Finite-Time Synchronization Analysis for BAM Neural Networks with Time-Varying Delays by Applying the Maximum-Value Approach with New Inequalities," Mathematics, MDPI, vol. 10(5), pages 1-16, March.
    18. Yang, Ni & Gao, Ruiyi & Su, Huan, 2022. "Stability of multi-links complex-valued impulsive stochastic systems with Markovian switching and multiple delays," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Stochastic Memristive Quaternion-Valued Neural Networks with Time Delays: An Analysis on Mean Square Exponential Input-to-State Stability," Mathematics, MDPI, vol. 8(5), pages 1-26, May.
    2. Tu, Zhengwen & Ding, Nan & Li, Liangliang & Feng, Yuming & Zou, Limin & Zhang, Wei, 2017. "Adaptive synchronization of memristive neural networks with time-varying delays and reaction–diffusion term," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 118-128.
    3. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    4. Mandic, D.P. & Javidi, S. & Goh, S.L. & Kuh, A. & Aihara, K., 2009. "Complex-valued prediction of wind profile using augmented complex statistics," Renewable Energy, Elsevier, vol. 34(1), pages 196-201.
    5. Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
    6. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
    7. Zhang, Lingzhong & Yang, Yongqing & Xu, Xianyun, 2018. "Synchronization analysis for fractional order memristive Cohen–Grossberg neural networks with state feedback and impulsive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 644-660.
    8. Took, C. Cheong & Strbac, G. & Aihara, K. & Mandic, D.P., 2011. "Quaternion-valued short-term joint forecasting of three-dimensional wind and atmospheric parameters," Renewable Energy, Elsevier, vol. 36(6), pages 1754-1760.
    9. Xu, Wei & Zhu, Song & Fang, Xiaoyu & Wang, Wei, 2019. "Adaptive anti-synchronization of memristor-based complex-valued neural networks with time delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    10. Sriraman, R. & Cao, Yang & Samidurai, R., 2020. "Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 103-118.
    11. Lin, Dongyuan & Chen, Xiaofeng & Yu, Guoping & Li, Zhongshan & Xia, Yannan, 2021. "Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses," Applied Mathematics and Computation, Elsevier, vol. 401(C).
    12. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Global Stability Analysis of Fractional-Order Quaternion-Valued Bidirectional Associative Memory Neural Networks," Mathematics, MDPI, vol. 8(5), pages 1-27, May.
    13. Mathiyalagan, K. & Park, Ju H. & Sakthivel, R., 2015. "Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 967-979.
    14. Huaiqin Wu & Luying Zhang & Sanbo Ding & Xueqing Guo & Lingling Wang, 2013. "Complete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-12, July.
    15. Ren, Yue & Jiang, Haijun & Hu, Cheng & Li, Xinman & Qin, Xuejiao, 2023. "Discontinuous control for exponential synchronization of complex-valued stochastic multi-layer networks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    16. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    17. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    18. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.
    19. 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.
    20. Zhang, Lingzhong & Yang, Yongqing & Wang, Fei, 2017. "Projective synchronization of fractional-order memristive neural networks with switching jumps mismatch," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 402-415.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:311:y:2017:i:c:p:100-117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.