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Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method

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Abstract

This paper studies the exponential anti-synchronization problem of memristive delayed neural networks under the event-triggered controller. To reduce the recalculation of the control signals, two event-triggered control strategies including static and dynamic are proposed. A novel Lyapunov function is constructed to analyze the global exponential anti-synchronization problem. By analysis, we can choose the suitable parameter of the controller to realize global exponential anti-synchronization with a given convergence rate γ without wasting a lot of control resources. Moreover, under event-triggering conditions given in our theorem, we derive that the Zeno behavior will not happen. Finally, numerical examples are given to validate our theorem.

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References

  1. Abdurahman A, Jiang H, Teng Z (2015) Finite-time synchronization for memristor-based neural networks with time-varying delays. Neural Netw 69:20–28

    MATH  Google Scholar 

  2. Cao Y, Cao Y, Wen S, Zeng Z, Huang T (2019) Passivity analysis of reaction–diffusion memristor-based neural networks with and without time-varying delays. Neural Netw 109:159–167

    MATH  Google Scholar 

  3. Chen G, Zhou J, Liu Z (2004) Global synchronization of coupled delayed neural networks and applications to chaotic CNN models. Int J Bifurc Chaos 14(07):2229–2240

    MathSciNet  MATH  Google Scholar 

  4. Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519

    Google Scholar 

  5. Dong M, Wen S, Zeng Z, Yan Z, Huang T (2019) Sparse fully convolutional network for face labeling. Neurocomputing 331:465–472

    Google Scholar 

  6. Fan Y, Huang X, Shen H, Cao J (2019) Switching event-triggered control for global stabilization of delayed memristive neural networks: an exponential attenuation scheme. Neural Netw 117:216–224

    MATH  Google Scholar 

  7. Feng Z, Niu W, Cheng C (2019) China’s large-scale hydropower system: operation characteristics, modeling challenge and dimensionality reduction possibilities. Renew Energy 136:805–818

    Google Scholar 

  8. Feng Z, Niu W, Zhang R, Wang S, Zhou J, Cheng C (2019) Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. J Hydrol 576:229–238

    Google Scholar 

  9. Gong S, Yang S, Guo Z, Huang T (2018) Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller. Neural Netw 102:138–148

    MATH  Google Scholar 

  10. Guo Z, Gong S, Huang T (2018) Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control. Neurocomputing 108:260–271

    MATH  Google Scholar 

  11. Guo Z, Gong S, Wen S, Huang T (2019) Event-based synchronization control for memristive neural networks with time-varying delay. IEEE Trans Cybern 49(9):3268–3277

    Google Scholar 

  12. Guo Z, Gong S, Yang S, Huang T (2018) Global exponential synchronization of multiple coupled inertial memristive neural networks with time-varying delay via nonlinear coupling. Neural Netw 108:260–271

    MATH  Google Scholar 

  13. Guo Z, Liu L, Wang J (2019) Event based synchronization control for memristive neural networks with time-varying delay. IEEE Trans Neural Netw Learn Syst 30:2052–2066

    MathSciNet  Google Scholar 

  14. Guo Z, Wang J, Yan Z (2013) Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays. Neural Netw 48:158–172

    MATH  Google Scholar 

  15. Guo Z, Wang J, Yan Z (2014) Passivity and passification of memristor-based recurrent neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst 25(11):2099–2109

    Google Scholar 

  16. Itoh M, Chua L (2014) Memristor cellular automata and memristor discrete-time cellular neural networks. In: Memristor lworks. Springer, pp 649–713

  17. Lakshmanan S, Prakash M, Lim CP, Rakkiyappan R, Balasubramaniam P, Nahavandi S (2016) Synchronization of an inertial neural network with time-varying delays and its application to secure communication. IEEE Trans Neural Netw Learn Syst 29(1):195–207

    MathSciNet  Google Scholar 

  18. Leen G, Heffernan D (2001) Time-triggered controller area network. Comput Control Eng J 12(6):245–256

    Google Scholar 

  19. Li C, Zhang Y, Xie EY (2019) When an attacker meets a cipher-image in 2018: a year in review. J Inf Secur Appl 48:102361

    Google Scholar 

  20. Li J, Hu M, Guo L (2014) Exponential stability of stochastic memristor-based recurrent neural networks with time-varying delays. Neurocomputing 138:92–98

    Google Scholar 

  21. Li N, Cao J (2015) Lag synchronization of memristor-based coupled neural networks via \(\omega \)-measure. IEEE Trans Neural Netw Learn Syst 27(3):686–697

    MathSciNet  Google Scholar 

  22. Li X, Li X, Hu C (2017) Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method. Neural Netw 96:91–100

    MATH  Google Scholar 

  23. Li X, Rakkiyappan R, Velmurugan G (2015) Dissipativity analysis of memristor-based complex-valued neural networks with time-varying delays. Inf Sci 294:645–665

    MathSciNet  MATH  Google Scholar 

  24. Li Z, Dong M, Wen S, Hu X, Zhou P, Zeng Z (2019) CLU-CNNs: object detection for medical images. Neurocomputing 350:53–59

    Google Scholar 

  25. Nghiem T, Pappas GJ, Alur R, Girard A (2006) Time-triggered implementations of dynamic controllers. In: Proceedings of the 6th ACM and IEEE international conference on embedded software. ACM, pp 2–11

  26. Niu W, Feng Z, Cheng C, Zhou J (2018) Forecasting daily runoff by extreme learning machine based on quantum-behaved particle swarm optimization. J Hydrol Eng ASCE 23(3):1–15

    Google Scholar 

  27. Niu W, Feng Z, Min Y, Feng B, Cheng C, Zhou J (2019) Comparison of multiple linear regression, artificial neural network, extreme learning machine and support vector machine in deriving hydropower reservoir operation rule. Water 11(1):88–100

    Google Scholar 

  28. Niu W, Feng Z, Zeng M, Feng B, Min Y, Cheng C, Zhou J (2019) Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Appl Soft Comput 82(105589):1–11

    Google Scholar 

  29. Rakkiyappan R, Chandrasekar A, Cao J (2014) Passivity and passification of memristor-based recurrent neural networks with additive time-varying delays. IEEE Trans Neural Netw Learn Syst 26(9):2043–2057

    MathSciNet  Google Scholar 

  30. Ren G, Cao Y, Wen S, Zeng Z, Huang T (2018) A modified elman neural network with a new learning rate. Neurocomputing 286:11–18

    Google Scholar 

  31. Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453(7191):80

    Google Scholar 

  32. Tang Z, Park JH, Feng J (2017) Impulsive effects on quasi-synchronization of neural networks with parameter mismatches and time-varying delay. IEEE Trans Neural Netw Learn Syst 29(4):908–919

    Google Scholar 

  33. Wang S, Cao Y, Huang T, Chen Y, Wen S (2020) Event-triggered synchronization of multiple memristive neural networks with cyber-physical attacks. Inf Sci 518:361–375

    Google Scholar 

  34. Wang S, Cao Y, Huang T, Wen S (2019) Passivity and passification of memristive neural networks with leakage term and time-varying delays. Appl Math Comput 361:294–310

    MathSciNet  MATH  Google Scholar 

  35. Wang S, Guo Z, Wen S, Huang T (2019) Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks. Neurocomputing 375:1–8

    Google Scholar 

  36. Wang Y, Cao Y, Guo Z, Wen S (2020) Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse. Appl Math Comput 369:1–11

    MathSciNet  MATH  Google Scholar 

  37. Wei L, Ding Y, Su R, Tang J, Zou Q (2018) Prediction of human protein subcellular localization using deep learning. J Parallel Distrib Comput 117:212–217

    Google Scholar 

  38. Wen S, Chen MZ, Yu X, Zeng Z, Huang T (2017) Fuzzy control for uncertain vehicle active suspension systems via dynamic sliding-mode approach. IEEE Trans Syst Man Cybern Syst 47:24–32

    Google Scholar 

  39. Wen S, Dong M, Yang Y, Zhou P, Huang T, Chen Y (2019) End-to-end detection-segmentation network for face labeling. IEEE Trans Emerg Top Comput Intell 99:1–11

    Google Scholar 

  40. Wen S, Hu R, Yang Y, Zeng Z, Huang T, Song Y-D (2018) Memristor-based echo state network with online least mean square. IEEE Trans Syst Man Cybern Syst 49(9):1787–1796

    Google Scholar 

  41. Wen S, Huang T, Yu X, Chen MZ, Zeng Z (2016) Aperiodic sampled-data sliding-mode control of fuzzy systems with communication delays via the event-triggered method. IEEE Trans Fuzzy Syst 24:1048–1057

    Google Scholar 

  42. Wen S, Liu W, Yang Y, Zeng Z, Huang T (2019) Generating realistic videos from keyframes with concatenated GANs. IEEE Trans Circuits Syst Video Technol 29:2337–2348

    Google Scholar 

  43. Wen S, Liu W, Yang Y, Zhou P, Yan Z, Guo Z, Chen Y, Huang T (2020) Multi-label image classification via feature/label co-projection. IEEE Trans Syst Man Cybern Syst 99:1–10

    Google Scholar 

  44. Wen S, Wei H, Yang Y, Guo Z, Zeng Z, Huang T, Chen Y (2019) Memristive LSTM networks for sentiment analysis. IEEE Trans Syst Man Cybern Syst 99:1–11

    Google Scholar 

  45. Wen S, Xiao S, Yang Y, Yan Z, Zeng Z, Huang T (2019) Adjusting the learning rate of memristor-based multilayer neural networks via fuzzy method. IEEE Trans Comput Aided Des Integr Circuits Syst 38(6):1084–1094

    Google Scholar 

  46. Wen S, Xie X, Yan Z, Huang T, Zeng Z (2018) General memristor with applications in multilayer neural networks. Neural Netw 103:142–148

    Google Scholar 

  47. Wen S, Zeng Z, Chen MZ, Huang T (2016) Synchronization of switched neural networks with communication delays via the event-triggered control. IEEE Trans Neural Netw Learn Syst 28(10):2334–2343

    MathSciNet  Google Scholar 

  48. Wen S, Zeng Z, Huang T, Zhang Y (2013) Exponential adaptive lag synchronization of memristive neural networks via fuzzy method and applications in pseudorandom number generators. IEEE Trans Fuzzy Syst 22(6):1704–1713

    Google Scholar 

  49. Yan Z, Liu W, Wen S, Yang Y (2019) Multi-label image classification by feature attention network. IEEE Access 99:1–9

    Google Scholar 

  50. Zeng X, Wang W, Deng G, Bing J, Zou Q (2019) Prediction of potential disease-associated micrornas by using neural network. Mol Ther Nucleic Acids 16:566–575

    Google Scholar 

  51. Zhang G, Shen Y (2014) Exponential stabilization of memristor-based chaotic neural networks with time-varying delays via intermittent control. IEEE Trans Neural Netw Learn Syst 26(7):1431–1441

    MathSciNet  Google Scholar 

  52. Zhang Z, Cao J (2018) Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Trans Neural Netw Learn Syst 30(5):1476–1485

    MathSciNet  Google Scholar 

  53. Zhang Z, Chen M, Li A (2020) Further study on finite-time synchronization for delayed inertial neural networks via inequality skills. Neurocomputing 373:15–23

    Google Scholar 

  54. Zhang Z, Li A, Yu S (2018) Finite-time synchronization for delayed complex-valued neural networks via integrating inequality method. Neurocomputing 318:248–260

    Google Scholar 

  55. Zhou B, Liao X, Huang T, Chen G (2015) Pinning exponential synchronization of complex networks via event-triggered communication with combinational measurements. Neurocomputing 157:199–207

    Google Scholar 

  56. Zou Q, Xing P, Wei L, Liu B (2019) Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA. RNA 25(2):205–218

    Google Scholar 

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Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant No. 61673187).

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiaoze Ni & Shiping Wen

  2. School of Mathematics and Econometrics, Hunan University, Changsha, 410082, China

    Yuting Cao & Zhenyuan Guo

  3. Science Program, Texas A&M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

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  1. Xiaoze Ni
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  5. Shiping Wen
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Correspondence to Shiping Wen.

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Ni, X., Cao, Y., Guo, Z. et al. Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method. Neural Comput & Applic 32, 13521–13535 (2020). https://doi.org/10.1007/s00521-020-04762-5

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  • DOI: https://doi.org/10.1007/s00521-020-04762-5

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