×

Quasi-uniform synchronization of Caputo type fractional neural networks with leakage and discrete delays. (English) Zbl 1510.34167

Summary: This paper discusses the quasi-uniform synchronization issue for fractional-order neural networks (FONNs) with leakage and discrete delays. The impacts of leakage delay, discrete delay and fractional derivative on the quasi-uniform synchronization are simultaneously considered. By employing the Laplace transformation, the Gronwall inequality and analytical techniques, several sufficient criteria of the quasi-uniform synchronization for FONNs with leakage and discrete delays are established. The criterion conditions reveal the less conservatism because the order of fractional derivative is in the interval (0,2). The presented results are related to the classical exponential function, where it is not necessary to calculate fractional-order derivatives to reduce the complexities. Taking into account the different orders of fractional derivative, the validity and applicability of the proposed results are verified by the numerical simulations.

MSC:

34K24 Synchronization of functional-differential equations
34K37 Functional-differential equations with fractional derivatives
44A10 Laplace transform
92B20 Neural networks for/in biological studies, artificial life and related topics
Full Text: DOI

References:

[1] Kilbas, A. A.; Srivastava, H. M.; Trujillo, J. J., Theory and applications of fractional differential equations (2006), Elsevier: Elsevier NorthHolland · Zbl 1092.45003
[2] Adolfsson, K.; Enelund, M.; Olsson, P., On the fractional order model of viscoelasticity, Mechanics of Time-Dependent Materials, 9, 15-34 (2005)
[3] Li, X.; Tian, X., Fractional order thermo-viscoelastic theory of biological tissue with dual phase lag heat conduction model, Appl Math Model, 95, 612-622 (2021) · Zbl 1481.74560
[4] Momani, S.; Odibat, Z., Analytical approach to linear fractional partial differential equations arising in flfluid mechanics, Phys Lett A, 355, 271-279 (2006) · Zbl 1378.76084
[5] Yousef, F. B.; Yousef, A.; Maji, C., Effects of fear in a fractional-order predator-prey system with predator density-dependent prey mortality, Chaos, Solitons and Fractals, 145 (2021) · Zbl 1498.92182
[6] Das, M.; Samanta, G. P., A delayed fractional order food chain model with fear effect and prey refuge, Math Comput Simul, 178, 218-245 (2020) · Zbl 1524.92069
[7] Anastassiou, G. A., Fractional neural network approximation, Computer and Mathematics with Applications, 64, 1655-1676 (2012) · Zbl 1268.41007
[8] Bao, H.; Park, J. H.; Cao, J., Adaptive synchronization of fractional-order output-coupling neural networks via quantized output control, IEEE Trans Neural Netw Learn Syst, 32, 3230-3239 (2021)
[9] Zhang, H.; Ye, M.; Ye, R.; Cao, J., Synchronization stability of riemann-liouville fractional delay-coupled complex neural networks, Physica A, 508, 155-165 (2018) · Zbl 1514.34126
[10] Zhang, H.; Ye, R.; Liu, S.; Cao, J.; Alsaedi, A.; Li, X., LMI-Based approach to stability analysis for fractional-order neural networks with discrete and distributed delays, Int J Syst Sci, 49, 537-545 (2018) · Zbl 1385.93067
[11] Zhang, H.; Ye, R.; Cao, J.; Alsaedi, A., Delay-independent stability of riemann-liouville fractional neutral-type delayed neural networks, Neural Processing Letters, 47, 427-442 (2018)
[12] Zhang, H.; Ye, R.; Cao, J.; Alsaedi, A.; Li, X.; Wan, Y., Lyapunov functional approach to stability analysis of riemann-liouville fractional neural networks with time-varying delays, Asian J Control, 20, 1938-1951 (2018) · Zbl 1407.93348
[13] Muthukumar, P.; Balasubramaniam, P., Feedback synchronization of the fractional order reverse butterfly-shaped chaotic system and its application to digital cryptography, Nonlinear Dyn, 74, 1169-1181 (2013) · Zbl 1284.34065
[14] Bondarenko, V., Information processing, memories, and synchronization in chaotic neural network with the time delay, Complexity, 11, 39-52 (2005)
[15] Alimi, A. M.; Aouiti, C.; Assali, E. A., Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication, Complexity, 332, 29-43 (2019)
[16] Yang, X.; Li, X.; Lu, J.; Cheng, Z., Synchronization of time-delayed complex networks with switching topology via hybrid actuator fault and impulsive effects control, IEEE Trans Cybern, 50, 4043-4052 (2020)
[17] Yang, X.; Liu, Y.; Cao, J.; Rutkowski, L., Synchronization of coupled time-delay neural networks with mode-dependent average dwell time switching, IEEE Trans Neural Netw Learn Syst, 31, 5483-5496 (2020)
[18] Tang, R.; Su, H.; Zou, Y.; Yang, X., Finite-time synchronization of markovian coupled neural networks with delays via intermittent quantized control: linear programming approach, IEEE Trans Neural Netw Learn Syst (2021)
[19] Xiao, J.; Cao, J.; Cheng, J.; Zhong, S.; Wen, S., Novel methods to finite-time mittag-leffler synchronization problem of fractional-order quaternion-valued neural networks, Inf Sci (Ny), 526, 221-244 (2020) · Zbl 1458.34102
[20] Zhang, W.; Cao, J.; Wu, R.; Chen, D.; Alsaadi, F. E., Novel results on projective synchronization of fractional-order neuraln networks with multiple time delays, Chaos, Solitons and Fractals, 117, 76-83 (2018) · Zbl 1442.93019
[21] Wang, S.; Zhang, H.; Zhang, W.; Zhang, H., Finite-time projective synchronization of caputo type fractional complex-valued delayed neural networks, Mathematics, 9, 1406 (2021)
[22] Li, H.; Hu, C.; Cao, J.; Jiang, H.; Alsaedi, A., Quasi-projective and complete synchronization of fractional-order complex-valued neural networks with time delays, Neural Networks, 118, 102-109 (2019) · Zbl 1443.93049
[23] Zhang, W.; Zhang, H.; Cao, J.; Zhang, H.; Alsaadi, F. E.; Alsaadi, A., Global projective synchronization in fractional-order quaternion valued neural networks, Asian J Control (2020)
[24] Yao, Z.; Zhou, P.; Zhu, Z.; Ma, J., Phase synchronization between a light-dependent neuron and a thermosensitive neuron, Neurocomputing, 423, 518-534 (2021)
[25] Yang, X.; Feng, Z.; Feng, J.; Cao, J., Synchronization of discrete-time neural networks with delays and markov jump topologies based on tracker information, Neural networks, 85, 157-164 (2017) · Zbl 1429.93346
[26] Yang, X.; Li, C.; Huang, T.; Song, Q.; Huang, J., Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays, Chaos, Solitons and Fractals, 110, 105-123 (2018) · Zbl 1391.93168
[27] Long, C.; Zhang, G.; Zeng, Z.; Hua, J., Finite-time lag synchronization of inertial neural networks with mixed infinite time-varying delays and state-dependent switching, Neurocomputing, 433, 50-58 (2021)
[28] Zhang, H.; Ye, R.; Cao, J.; Alsaedi, A., Synchronization control of riemann-liouville fractional competitive network systems with time-varying delay and different time scales, Int J Control Autom Syst, 16, 1404-1414 (2018)
[29] Lakshmanan, S.; Park, J.; Jung, H.; Balasubramaniam, P., Design of state estimator for neural networks with leakage, discrete and distributed delays, Appl Math Comput, 218, 11297-11310 (2012) · Zbl 1277.93078
[30] Gopalsamy, K., Leakage delays in BAM, J Math Anal Appl, 325, 1117-1132 (2007) · Zbl 1116.34058
[31] Huang, C.; Cao, J., Impact of leakage delay on bifurcation in high-order fractional BAM neural networks, Neural Networks, 98, 223-235 (2018) · Zbl 1439.93004
[32] Yang, X.; Li, C.; Huang, T.; Song, Q.; Chen, X., Quasi-uniform synchronization of fractional-order memristor-based neural networks with delay, Neurocomputing, 234, 205-215 (2017)
[33] Wu, H.; Zhang, X.; Xue, S.; Niu, P., Quasi-uniform stability of caputo-type fractional-order neural networks with mixed delay, Int J Mach Learn Cybern, 8, 1501-1511 (2017)
[34] Velmurugan, G.; Rakkiyappan, R.; Cao, J., Finite-time synchronization of fractional-order memristor-based neural networks with time delays, Neural Networks, 73, 36-46 (2016) · Zbl 1398.34110
[35] Du, F.; Lu, J., New criteria for finite-time stability of fractional order memristor-based neural networks with time delays, Neurocomputing, 421, 349-359 (2021)
[36] Yang, H.; Wang, Z.; Xiao, M.; Jiang, G.; Huang, C., Quasi-synchronization of heterogeneous dynamical networks with sampled-data and input saturation, Neurocomputing, 339, 130-138 (2019)
[37] Gu, Y.; Yu, Y.; Wang, H., Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty, J Franklin Inst, 353, 3657-3684 (2016) · Zbl 1347.93013
[38] Hu, T.; He, Z.; Zhang, X.; Zhong, S., Finite-time stability for fractional-order complex-valued neural networks with time delay, Appl Math Comput, 365 (2020) · Zbl 1433.34097
[39] Liu, Y.; Xu, P.; Lu, J.; Liang, J., Global stability of clifford-valued recurrent neural networks with time delays, Nonlinear Dyn, 84, 767-777 (2016) · Zbl 1354.93132
[40] Liu, Y.; Zheng, J.; Lu, J.; Cao, J.; Rutkowski, L., Constrained quaternion-variable convex optimization: a quaternion-valued recurrent neural network approach, IEEE Trans Neural Netw Learn Syst, 31, 1022-1035 (2020)
[41] Xia, Z.; Liu, Y.; Lu, J.; Cao, J.; Rutkowski, L., Penalty method for constrained distributed quaternion-variable optimization, IEEE Trans Cybern (2020)
[42] Qin, S.; Feng, J.; Song, J.; Wen, X.; Xu, C., A one-layer recurrent neural network for constrained complex-variable convex optimization, IEEE Trans Neural Netw Learn Syst, 29, 534-544 (2018)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.