The synchronization and stability analysis of delayed fuzzy Cohen-Grossberg neural networks via nonlinear measure method

References

• Abdelaziz, M., & Chàif, F. (2020a). Exponential Lag Synchronization and Global Dissipativity for Delayed Fuzzy Cohen–Grossberg Neural Networks with Discontinuous Activations. Neural Process Lett, 51(2), 1653–1676. http://doi.org/10.1007/s11063-019-10169-8
   Web of Science ®Google Scholar
• Abdurahman, A., Cheng, H., & Haijun, J. (2015). Exponential lag synchronization for delayed Cohen-Grossberg neural networks with discontinuous activations (p. 129–137). Springer International Publishing. LNCS 9377.
   Google Scholar
• Abdurahman, A., Jiang, H., & Teng, Z. (2016a). Exponential lag synchronization for memristor-based neural networks with mixed time delays via hybrid switching control. Journal of the Franklin Institute, 353(13), 2859–2880. http://doi.org/10.1016/j.jfranklin.2016.05.022
   Web of Science ®Google Scholar
• Abdurahman, A., Jiang, H., & Teng, Z. (2016b). Finite-time synchronization for fuzzy cellular neural networks with time-varying delays. Fuzzy Sets and Systems, 297(13), 96–111. http://doi.org/10.1016/j.fss.2015.07.009
   Web of Science ®Google Scholar
• Bao, H., Park, J. H., & Cao, J. (2015). Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays. In Applied Mathematics and Computation (Vol. 270, pp. 543–556).
   Google Scholar
• Brahmi, H., B. Ammar, F. Chérif & A. M. Alimi. (2017). Stability and exponential synchronization of high-order Hopfield neural networks with mixed delays. Cybernetics and systems, 48(1), 49–69. http://doi.org/10.1080/01969722.2016.1262709
   Web of Science ®Google Scholar
• Cao, J., & Wan, Y. (2014). Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays. Neural Networks, 53(C), 165–172. http://doi.org/10.1016/j.neunet.2014.02.003
   Web of Science ®Google Scholar
• Chàif, F., & Abdelaziz, M. (2020). Stepanov-Like Pseudo Almost Periodic Solution of Quaternion-Valued for Fuzzy Recurrent Neural Networks with Mixed Delays. Neural Process Lett, 51(2), 2211–2243. http://doi.org/10.1007/s11063-020-10193-z
   Web of Science ®Google Scholar
• Chàif, F., Brahmi, H., Ammar, B., & Alimi, A.-M. (2013). Exponential synchronization of a class of RNNs with discrete and distributed delays LNCS 8131. Springer-Verlag. 74–81.
   Google Scholar
• Cohen, M., & Grossberg, S. (1983). Stability and global pattern formation and memory storage by competitive neural networks, IEEE Trans. Syst. Man Cybern., 13(5), 815–826. http://doi.org/10.1109/TSMC.1983.6313075
   Web of Science ®Google Scholar
• Duan, L., & Huang, C. (2017). Existence and global attractivity of almost periodic solutions for a delayed differential neoclassical growth model. Mathematical methods in the applied sciences, 40(3), 814–822. http://doi.org/10.1002/mma.4019
   Web of Science ®Google Scholar
• Farzaneh, G., et al. (2020). Precise prediction of biogas thermodynamic properties by using ANN algorithm (Vol. 147, pp. 179–191).
   Google Scholar
• He, W., & Cao, J. (2009). Exponential synchronization of chaotic neural networks: a matrix measure approach. Nonlinear dynamics, 55(1), 55–65. http://doi.org/10.1007/s11071-008-9344-4
   Web of Science ®Google Scholar
• Heydar, M., Mahyar, G., & Mohammad, H. A. (2019). Predicting the efficiency of CuO/water nanofluid in heat pipe heat exchanger using neural network. International Communications in Heat and Mass Transfer, 104(1), 33–40. http://doi.org/10.1016/j.icheatmasstransfer.2019.02.002
   Web of Science ®Google Scholar
• Huang, C., & Zhang, H. (2019). Periodicity of non-autonomous inertial neural networks involving proportional delays and non-reduced order method. International Journal of Biomathematics, 12(2), 1950016. http://doi.org/10.1142/S1793524519500165
   Web of Science ®Google Scholar
• Jiang, Y., & Zhai, J. (2019). Further results on quasi-synchronisation of delayed chaotic systems with parameter mismatches via intermittent control. Asian journal of control, 21(1), 1–12. https://doi.org/10.1002/asjc.1738
   Google Scholar
• Jin, G., Liu, F., Hao, W., & Song, Q. (2019). Deep learning-based framework for expansion, recognition and classification of underwater acoustic signal. Journal of Experimental and Theoretical Artificial Intelligence, 32(2), 205–218. http://doi.org/10.1080/0952813X.2019.1647560
   Web of Science ®Google Scholar
• Kahani, M., Ahmadi, M. H., Tatar, A., & Sadeghzadeh, M. (2018). Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO2/water nanofluid flows through non-straight pathways. Numerical Heat Transfer, 74 (4), 1190–1206. Part A: Applications. http://doi.org/10.1080/10407782.2018.1523597
   Web of Science ®Google Scholar
• Konnur, R. (2003). Synchronization-based approach for estimating all model parameters of chaotic systems. Phys. Rev. E 67-027204.
   Google Scholar
• Lian, D., Hui, W., & Huang, L.-H. (2019). Finite-time synchronization of delayed fuzzy cellular neural networks with discontinuous activations, Fuzzy sets and systems,361 (pp. 56–70).
   Google Scholar
• Nahid, G. (2018). H. Maddah, R. Aghayari, M. Hossein Ahmadi, & M. Rahimzadeh; Eds. Prediction and modeling of MWCNT/Carbon (60/40)/SAE 10 W 40/SAE 85 W 90 (50/50) nanofluid viscosity using artificial neural network (ANN) and self-organizing map (SOM). Journal of Thermal Analysis and Calorimetry. Vol. 134. 3 2275–2286. http://doi.org/10.1007/s10973-018-7827-1
   Google Scholar
• Pan, L., Cao., J., & Hu, J. (2015). Synchronization for complex networks with Markov switching via matrix measure approach. Appl. Math. Model, 39(18), 5636–5649. http://doi.org/10.1016/j.apm.2015.01.027
   Web of Science ®Google Scholar
• Pecora, L. M., & Carroll, T. L. (1990). Synchronization in chaotic systems. Phys. Rev. Lett, 64(8), 821–824. http://doi.org/10.1103/PhysRevLett.64.821
   PubMed Web of Science ®Google Scholar
• Qiao, H., Jigen, P., & Zong-Ben, X. (2001). Nonlinear measures: a new approach to exponential stability analysis for Hopfield-type neural networks. IEEE Transactions on Neural Networks, 12(2), 360–370. http://doi.org/10.1109/72.914530
   Google Scholar
• Wang, C. C., & Su, J. P. (2004). A new adaptive variable structure control for chaotic synchronization and secure communication. Chaos, solitons, and fractals, 20(5), 967–977. http://doi.org/10.1016/j.chaos.2003.10.026
   Web of Science ®Google Scholar
• Wang, L., Ding., W., & Chen, D. (2010). Synchronization schemes of a class of fuzzy cellular neural networks based on adaptive control. Physics letters. A, 374(13–14), 1440–1449. http://doi.org/10.1016/j.physleta.2010.01.029
   Web of Science ®Google Scholar
• Xia, Y., Zijiang, Y., & Maoan, H. (2009). Synchronization schemes for coupled identical Yang-Yang type fuzzy cellular neural networks. Commun Nonlinear Sci Numer Simulat, 14(9–10), 3645–3659. http://doi.org/10.1016/j.cnsns.2009.01.028
   Web of Science ®Google Scholar
• Xing, Z., & Peng, J. (2012). Exponential lag synchronization of fuzzy cellular neural networks with time-varying delays. Journal of the Franklin Institute, 349(3), 1074–1086. http://doi.org/10.1016/j.jfranklin.2011.12.008
   Web of Science ®Google Scholar
• Yang, C., Huang, L., & Li, F. (2018). Exponential Synchronization Control of Discontinuous Nonautonomous Networks and Autonomous Coupled Networks. Complexity, 2018(1), 10. Article ID 6164786. http://doi.org/10.1155/2018/6164786.
   Google Scholar
• Yang, T., & Chua, L. O. (1997). Impulsive control and synchronization of nonlinear dynamical systems and application to secure communication. Int. J. Bifurcat. Chaos, 7(3), 645–664. http://doi.org/10.1142/S0218127497000443
   Web of Science ®Google Scholar
• Yang, T., & Yang, L. (1996). The global stability of fuzzy cellular neural networks. IEEE Trans. Cric. Syst. I, 43(10), 880–883. DOI:10.1109/81.538999
   Google Scholar
• Yang, W., et al. (2018). Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen-Grossberg BAM neural networks with impulses. Neural Networks, 98(1), 122–153. http://doi.org/10.1016/j.neunet.2017.11.001
   Web of Science ®Google Scholar
• Yang, Y., & Jinde, C. (2007). Exponential lag synchronization of a class of chaotic delayed neural networks with impulsive effects. Physica A, 386(1), 492–502. http://doi.org/10.1016/j.physa.2007.07.049
   Web of Science ®Google Scholar
• Yao, C., Zhao, Q., & Yu, J. (2013). Complete synchronization induced by disorder in coupled chaotic lattices. Phys. Lett. A, 377(5), 370–377. http://doi.org/10.1016/j.physleta.2012.12.004
   Web of Science ®Google Scholar
• Yu, J., Hu., C., Jiang., H., & Teng, Z. (2012). Exponential lag synchronization for delayed fuzzy cellular neural networks via periodically intermittent control. Mathematics and computers in simulation, 82(5), 895–908. http://doi.org/10.1016/j.matcom.2011.11.006
   Web of Science ®Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. http://doi.org/10.1016/S0019-9958(65)90241-X
   Google Scholar