Global asymptotic stability analysis for neutral-type complex-valued neural networks with random time-varying delays

References

• Aizenberg, I. (2011). Complex-valued neural networks with multi-valued neurons. New York, NY: Springer.
   Google Scholar
• Amin, M. F., & Murase, K. (2009). Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing, 72, 945–955. doi: 10.1016/j.neucom.2008.04.006
   Web of Science ®Google Scholar
• Bao, H., Park, J. H., & Cao, J. (2016). Synchronization of fractional-order complex-valued neural networks with time delay. Neural Networks, 81, 16–28. doi: 10.1016/j.neunet.2016.05.003
   PubMed Web of Science ®Google Scholar
• Chen, X., & Song, Q. (2013). Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales. Neurocomputing, 121, 254–264. doi: 10.1016/j.neucom.2013.04.040
   Web of Science ®Google Scholar
• Cheng, C. J., Liao, T. L., Yan, J. J., & Hwang, C. C. (2006). Globally asymptotic stability of a class of neutral-type neural networks with delays. IEEE Transactions on Systems, Man and Cybernetics – Part B, 36, 1191–1195. doi: 10.1109/TSMCB.2006.874677
   PubMed Web of Science ®Google Scholar
• Fang, T., & Sun, J. T. (2014). Stability of complex-valued recurrent neural networks with time-delays. IEEE Transactions on Neural Networks and Learning Systems, 25, 1709–1713. doi: 10.1109/TNNLS.2013.2294638
   Web of Science ®Google Scholar
• Feng, J.-E., Xu, S., & Zou, Y. (2019). Delay-dependent stability of neutral type neural networks with distributed delays. Neurocomputing, 72, 2576–2580. doi: 10.1016/j.neucom.2008.10.018
   Web of Science ®Google Scholar
• Goh, S. L., Chen, M., Popovic, D. H., Aihara, K., Obradovic, D., & Mandic, D. P. (2006). Complex-valued forecasting of wind profile. Renewable Energy, 31, 1733–1750. doi: 10.1016/j.renene.2005.07.006
   Web of Science ®Google Scholar
• Gong, W., Liang, J., & Cao, J. (2015a). Global μ-stability of complex-valued delayed neural networks with leakage delay. Neurocomputing, 168, 135–144. doi: 10.1016/j.neucom.2015.06.006
   Web of Science ®Google Scholar
• Gong, W., Liang, J., & Cao, J. (2015b). Matrix measure method for global exponential stability of complex-valued recurrent neural networks with time-varying delays. Neural Networks, 70, 81–89. doi: 10.1016/j.neunet.2015.07.003
   PubMed Web of Science ®Google Scholar
• Hirose, A. (2006). Complex-valued neural networks. Berlin: Springer.
   Google Scholar
• Hu, J., & Wang, J. (2012). Global stability of complex-valued recurrent neural networks with time-delays. IEEE Transactions on Neural Networks and Learning Systems, 23, 853–865. doi: 10.1109/TNNLS.2012.2195028
   PubMed Web of Science ®Google Scholar
• Jankowski, S., Lozowski, A., & Zurada, J. (1996). Complex-valued multistate neural associative memory. IEEE Transactions on Neural Networks, 7, 1491–1496. doi: 10.1109/72.548176
   PubMed Web of Science ®Google Scholar
• Kuroe, Y., Yoshid, M., & Mori, T. (2003). On activation functions for complex-valued neural networks-existence of energy functions. In: Artificial neural networks and neural information processing-ICANN/ICONIP 2003, 174–175.
   Google Scholar
• Liang, J., Li, K., Song, Q., Zhao, Z., Liu, Y., & Alsaadie, F. E. (2018). State estimation of complex-valued neural networks with two additive time-varying delays. Neurocomputing, 309, 54–61. doi: 10.1016/j.neucom.2018.05.003
   Web of Science ®Google Scholar
• Liu, P. L. (2013). Improved delay-dependent stability of neutral type neural networks with distributed delays. ISA Transactions, 52, 717–724. doi: 10.1016/j.isatra.2013.06.012
   PubMed Web of Science ®Google Scholar
• Liu, D., Zhu, S., & Chang, W. (2017). Mean square exponential input-to-state stability of stochastic memristive complex-valued neural networks with time-varying delay. International Journal of Systems Science, 48, 1966–1977. doi: 10.1080/00207721.2017.1300706
   Web of Science ®Google Scholar
• Liu, D., Zhu, S., & Ye, E. (2017). Global exponential periodicity and stability of memristor-based complex-valued delayed neural networks. International Journal of Systems Science, 49, 231–245. doi: 10.1080/00207721.2017.1397809
   Web of Science ®Google Scholar
• Luan, X., Shi, P., & Liu, F. (2011). Stabilization of networked control systems with random delays. IEEE Transactions on Industrial Electronics, 58, 4323–4330. doi: 10.1109/TIE.2010.2102322
   Web of Science ®Google Scholar
• Manivannan, R., Samidurai, R., Cao, J., Alsaedi, A., & Alsaadi, F. E. (2018). Design of extended dissipativity state estimation for generalized neural networks with mixed time-varying delay signals. Information Sciences, 424, 175–203. doi: 10.1016/j.ins.2017.10.007
   Web of Science ®Google Scholar
• Manivannan, R., Samidurai, R., & Sriraman, R. (2016). An improved delay-partitioning approach to stability criteria for generalized neural networks with interval time-varying delays. Neural Computing and Applications, 28, 3353–3369. doi: 10.1007/s00521-016-2220-0
   Web of Science ®Google Scholar
• Mathews, J., & Howell, R. (1997). Complex analysis for mathematics and engineering. Boston, MA: Jones and Bartlett.
   Google Scholar
• Mathiyalagan, K., Su, H., Shi, P., & Sakthivel, R. (2015). Exponential H∞ filtering for discrete-time switched neural networks with random delays. IEEE Transactions on Cybernetics, 45, 676–687. doi: 10.1109/TCYB.2014.2332356
   PubMed Web of Science ®Google Scholar
• Muralisankar, S., & Gopalakrishnan, N. (2014). Robust stability criteria for Takagi-sugeno fuzzy Cohen-Grossberg neural networks of neutral type. Neurocomputing, 144, 516–525. doi: 10.1016/j.neucom.2014.04.019
   Web of Science ®Google Scholar
• Muthukumar, P., Subramanian, K., & Lakshmanan, S. (2016). Robust finite time stabilization analysis for uncertain neural networks with leakage delay and probabilistic time-varying delays. Journal of the Franklin Institute, 353, 4091–4113. doi: 10.1016/j.jfranklin.2016.07.006
   Web of Science ®Google Scholar
• Nishikawa, I., Iritani, T., & Sakakibara, K. (2006). Improvements of the traffic signal control by complex-valued Hopfield networks. Proceedings of International Joint Conference on Neural Network Proceedings (pp. 459–464). doi:10.1109/IJCNN.2006.246717.
   Google Scholar
• Nishikawa, I., Sakakibara, K., Iritani, T., & Kuroe, Y. (2005). 2 Types of complex-valued Hopfield networks and the application to a traffic signal Control. Proceedings of International Joint Conference on Neural Network Proceedings (pp. 782–787). doi:10.1109/IJCNN.2005.1555951.
   Google Scholar
• Nitta, T. (2004). Orthogonality of decision boundaries in complex-valued neural networks. Neural Computation, 16, 73–97. doi: 10.1162/08997660460734001
   PubMed Web of Science ®Google Scholar
• Park, P. G., Ko, J. W., & Jeong, C. (2011). Reciprocally convex approach to stability of systems with time-varying delays. Automatica, 47, 235–238. doi: 10.1016/j.automatica.2010.10.014
   Web of Science ®Google Scholar
• Pradeep, C., Chandrasekar, A., Murugesu, R., & Rakkiyappan, R. (2014). Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time-varying delays. Complexity, 21, 59–72. doi: 10.1002/cplx.21630
   Web of Science ®Google Scholar
• Rajavel, S., Samidurai, R., Cao, J., Alsaedi, A., Alsaadi, F., & Ahmad, B. (2017). Delay-partitioning approach to stability analysis of state estimation for neutral-type neural networks with both time-varying delays and leakage term via sampled-data control. International Journal of Systems Science, 48, 1752–1765. doi: 10.1080/00207721.2017.1282060
   Web of Science ®Google Scholar
• Rakkiyappan, R., Velmurugan, G., & Cao, J. (2014). Multiple μ-stability analysis of complex-valued neural networks with unbounded time-varying delays. Neurocomputing, 149, 594–607. doi: 10.1016/j.neucom.2014.08.015
   Web of Science ®Google Scholar
• Ramasamy, S., & Nagamani, G. (2017). Dissipativity and passivity analysis for discrete-time complex-valued neural networks with leakage delay and probabilistic time-varying delays. International Journal of Adaptive Control and Signal Processing, 31, 876–902. doi: 10.1002/acs.2736
   Web of Science ®Google Scholar
• Samidurai, R., Rajavel, S., Sriraman, R., Cao, J., Alsaedi, A., & Alsaadi, F. E. (2016). Novel results on stability analysis of neutral-type neural networks with additive time-varying delay components and leakage delay. International Journal of Control, Automation and Systems, 15, 1888–1900. doi: 10.1007/s12555-016-9483-1
   Web of Science ®Google Scholar
• Samidurai, R., Rajavel, S., Zhu, Q., Raja, R., & Zhou, H. (2015). Robust passivity analysis for neutral-type neural networks with mixed and leakage delays. Neurocomputing, 175, 635–643. doi: 10.1016/j.neucom.2015.10.103
   Web of Science ®Google Scholar
• Samidurai, R., Sriraman, R., & Zhu, S. (2019). Leakage delay-dependent stability analysis for complex-valued neural networks with discrete and distributed time-varying delays. Neurocomputing, 338, 262–273. doi: 10.1016/j.neucom.2019.02.027
   Web of Science ®Google Scholar
• Shi, P., Li, F., Wu, L., & Lim, C. C. (2017). Neural network-based passive filtering for delayed neutral-type semi-Markovian jump systems. IEEE Transactions on Neural Networks and Learning Systems, 28, 2101–2114.
   PubMed Web of Science ®Google Scholar
• Shi, K., Zhong, S., Zhu, H., Liu, X., & Zeng, Y. (2015). New delay-dependent stability criteria for neutral-type neural networks with mixed random time-varying delays. Neurocomputing, 168, 896–907. doi: 10.1016/j.neucom.2015.05.035
   Web of Science ®Google Scholar
• Song, Q., Zhao, Z., & Liu, Y. (2015). Stability analysis of complex-valued neural networks with probabilistic time-varying delays. Neurocomputing, 159, 96–104. doi: 10.1016/j.neucom.2015.02.015
   Web of Science ®Google Scholar
• Tu, Z., Cao, J., Alsaedi, A., Alsaadi, F. E., & Hayat, T. (2016). Global Lagrange stability of complex-valued neural networks of neutral type with time-varying delays. Complexity, 21, 438–450. doi: 10.1002/cplx.21823
   Web of Science ®Google Scholar
• Wang, Z., & Huang, L. (2016). Global stability analysis for delayed complex-valued BAM neural networks. Neurocomputing, 173, 2083–2089. doi: 10.1016/j.neucom.2015.09.086
   Web of Science ®Google Scholar
• Xu, S., Shi, P., Chu, Y., & Zou, Y. (2006). Robust stochastic stabilization and H∞ control of uncertain neutral stochastic time-delay systems. Journal of Mathematical Analysis and Applications, 314, 1–16. doi: 10.1016/j.jmaa.2005.03.088
   Web of Science ®Google Scholar
• Xu, X., Zhang, J., & Shi, J. (2014). Exponential stability of complex-valued neural networks with mixed delays. Neurocomputing, 128, 483–490. doi: 10.1016/j.neucom.2013.08.014
   Web of Science ®Google Scholar
• Xu, X., Zhang, J., & Shi, J. (2016). Dynamical behaviour analysis of delayed complex-valued neural networks with impulsive effect. International Journal of Systems Science, 48, 686–694. doi: 10.1080/00207721.2016.1206988
   Web of Science ®Google Scholar
• Yang, H., & Chu, T. (2007). LMI conditions for stability of neural networks with distributed delays. Chaos, Solitons, and Fractals, 34, 557–563. doi: 10.1016/j.chaos.2006.03.072
   Web of Science ®Google Scholar
• Yang, R., Shi, P., & Liu, G. P. (2011). Filtering for discrete-time networked nonlinear systems with mixed random delays and packet dropouts. IEEE Transactions on Automatic Control, 56, 2655–2660. doi: 10.1109/TAC.2011.2166729
   Web of Science ®Google Scholar
• Zhang, Z., Lin, C., & Chen, B. (2014). Global stability criterion for delayed complex-valued recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems, 25, 1704–1708. doi: 10.1109/TNNLS.2013.2288943
   Web of Science ®Google Scholar
• Zhang, J., Shi, P., & Qiu, J. (2008). Robust stability criteria for uncertain neutral system with time delay and nonlinear uncertainties. Chaos, Solitons & Fractals, 38, 160–167. doi: 10.1016/j.chaos.2006.10.068
   Web of Science ®Google Scholar
• Zhang, Z., & Yu, S. (2016). Global asymptotic stability for a class of complex-valued Cohen-Grossberg neural networks with time delays. Neurocomputing, 171, 1158–1166. doi: 10.1016/j.neucom.2015.07.051
   Web of Science ®Google Scholar
• Zhou, B., & Song, Q. (2013). Boundedness and complete stability of complex-valued neural networks with time delay. IEEE Transactions on Neural Networks and Learning Systems, 24, 1227–1238. doi: 10.1109/TNNLS.2013.2247626
   PubMed Web of Science ®Google Scholar