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SCIENCE CHINA Information Sciences, Volume 63 , Issue 11 : 212201(2020) https://doi.org/10.1007/s11432-019-2664-7

Variable-sampling-period dependent global stabilization of delayed memristive neural networks based on refined switching event-triggered control

Zhilian YAN 1, Xia HUANG 1,*, Jinde CAO 2
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  • ReceivedJun 11, 2019
  • AcceptedSep 2, 2019
  • PublishedOct 9, 2020
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Abstract


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61973199, 61473178, 61573008). We would thank anonymous reviewers for their valuable suggestions.


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  • Click to view Download high-quality image

    Figure 1

    (Color online) The chaotic attractor of system (1) with initial conditions $(~-1.3,0.8)~^{\rm~T}$.

  • Table 1  

    Table 1Comparison of triggering times $t_{s}$ for RSETC and SETC schemes at $\tilde{h}_{k}~=~0.06$
    Method Parameters $t_{s}$ $m_{s}$ (%)
    SETC [33-35] $\alpha~=0$, $\beta~=1$ $130$ $100$
    RSETC $\alpha~=0.01$, $\beta~=1$ $55$ $42$
    RSETC $\alpha~=0.1$, $\beta~=1$ $44$ $33$
  • Table 2  

    Table 2Comparison of triggering times $t_{s}$ for different $\beta$ at $\tilde{h}_{k}~=~0.06$
    Method Parameters $t_{s}$ $m_{s}$ (%)
    RSETC $\alpha~=0.1$, $\beta~=5$ $127$ $100$
    RSETC $\alpha~=0.1$, $\beta~=2$ $93$ $73$
    RSETC $\alpha~=0.1$, $\beta~=1$ $44$ $34$
  • Table 3  

    Table 3Comparison of the triggering times $t_{s}$ between RSETC and SETC schemes at $\tilde{h}_{k}~=~0.014$
    Method Parameters $t_{s}$ $m_{s}$ (%)
    SETC [33-35] $\alpha~=0,\beta=5$ $333$ $100$
    RSETC $\alpha~=0.001$, $\beta=5$ $235~$ $70~$
    RSETC $\alpha~=0.01$, $\beta=5$ $227~$ $68~$
  • Table 4  

    Table 4Comparison of triggering times $t_{s}$ for different $\beta$ at $\tilde{h}_{k}~=~0.014$
    Method Parameters $t_{s}$ $m_{s}$ (%)
    RSETC $\alpha~=0.01$, $\beta=7$ $290$ $100$
    RSETC $\alpha~=0.01$, $\beta=5$ $227~$ $78~$
    RSETC $\alpha~=0.01$, $\beta=3$ $170~$ $58~$