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Time of the last modification database: 2020-10-27 15:44:51
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  1. Yang, Y., Wang, L., Duan, S., Luo, L., Dynamical analysis and image encryption application of a novel memristive hyperchaotic system. Optics & Laser Technology, vol.133, pp.1-15, January 2021.
  2. Du, F., Lu, J.-G., New criterion for finite-time synchronization of fractional order memristor-based neural networks with time delay. Applied Mathematics and Computation, vol.389, pp.1-16, January 2021.
  3. Zhang, B., Uysal, N., Fan, D., Ewetz, R., Redundant Neurons and Shared Redundant Synapses for Robust Memristor-based DNNs with Reduced Overhead. GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI, pp.339-344, 7th December 2020.
  4. Rajchakit, G., Chanthorn, P., Chanthorn, M., Raja, R., Baleanu, D., Pratap, A., Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks. Neurocomputing, vol.417, pp.290-301, 5th December 2020.
  5. Ryu, J.-H., Hussain, F., Mahata, C., Ismail, M., Abbas, Y., Kim, M.-H., Choi, C., Park, B.-G., Kim, S., Filamentary and interface switching of CMOS-compatible Ta2O5 memristor for non-volatile memory and synaptic devices. Applied Surface Science, vol.529, pp.1-7, 1st November 2020.
  6. Ryu, J.-H., Kim, S., Artificial synaptic characteristics of TiO2/HfO2 memristor with self-rectifying switching for brain-inspired computing. Chaos, Solitons & Fractals, vol.140, pp.1-8, November 2020.
  7. Liu, X., Zeng, Z., Wunsch, D.C., Memristor-based LSTM network with in situ training and its applications. Neural Networks, vol.131, pp.300-311, November 2020.
  8. Konal, M., Kaçar, F., Electronically Tunable Meminductor Based on OTA. AEU - International Journal of Electronics and Communications, vol.126, pp.1-9, November 2020.
  9. Jahanshahi, H., Yousefpour, A., Munoz-Pacheco, J.M., Kacar, S., Pham, V.-T., Alsaadi, F.E., A new fractional-order hyperchaotic memristor oscillator: Dynamic analysis, robust adaptive synchronization, and its application to voice encryption. Applied Mathematics and Computation, vol.383, pp.1-15, 15th October 2020.
  10. Zheng, C.-D., Zhang, L., On synchronization of competitive memristor-based neural networks by nonlinear control. Neurocomputing, vol.410, pp.151-160, 14th October 2020.
  11. Ren, H., Peng, Z., Gu, Y., Fixed-time synchronization of stochastic memristor-based neural networks with adaptive control. Neural Networks, vol.130, pp.165-175, October 2020.
  12. Haron, M.A., Halim, A.K., Osman, F.N., Razak, A.H.A., Idros, M.F.M., Zain, W.M.I.W., Al Junid, S.A.M., Simulation study of memristor aided logic (magic) based on CMOS NOR gate. Bulletin of Electrical Engineering and Informatics, vol.9, no.5, pp.2134-2140, October 2020.
  13. Dragoman, M., Dinescu, A., Dragoman, D., Palade, C., Moldovan, A., Dinescu, M., Teodorescu, V.S., Ciurea, M.L., Wafer-scale graphene-ferroelectric hfo2/ge-hfo2/hfo2 transistors acting as three-terminal memristors. Nanotechnology, vol.31, no.49, pp.1-10, 22nd September 2020.
  14. Getachew, M.N., Priyadarshini, R., Mehra, R.M., Memristive biophysical neuron models forming an excitatory–inhibitory neural network for modeling PING rhythm generation. Journal of Computational Electronics, pp.1-28, 11th September 2020.
  15. Zhao, G., Ke, X., Li, X., Wang, L., Yin, N., Jin, X., Chen, J., Xu, Y., Wang, K., Yu, X., Yu, Z., Memristor based on a layered FePS3 2D material with dual modes of resistive switching. Applied Physics Express, vol.13, no.10, pp.1-6, 11th September 2020.
  16. Li, X., Ge, Y., Liu, H., Li, H., Fang, J.-a., New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control. Complexity, vol.2020, no.2470972, pp.1-11, 9th September 2020.
  17. Zhu, Z., Sun, H., Qiu, K., Xia, L., Krishnan, G., Dai, G., Niu, D., Chen, X., Hu, X.S., Cao, Y., Xie, Y., Wang, Y., Yang, H., MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems. Proceedings of the 2020 on Great Lakes Symposium on VLSI, pp.83-88, 8th September 2020.
  18. Ascoli, A., Messaris, I., Tetzlaff, R., Kang, S., Chua, L.O., Image Mem-Processing Bio-Inspired Cellular Arrays with Bistable and Analogue Dynamic Memristors. 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp.1-6, 7th September 2020.
  19. Mladenov, V., A Modified Tantalum Oxide Memristor Model for Neural Networks with Memristor-Based Synapses. 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp.1-4, 7th September 2020.
  20. Chiu, C.-F., Ginnaram, S., Senapati, A., Chen, Y.-P., Maikap, S., Switching Characteristics and Mechanism Using Al2O3 Interfacial Layer in Al/Cu/GdOx/Al2O3/TiN Memristor. Electronics, vol.9, no.9, pp.1-17, 7th September 2020.
  21. Roy, S., Ghosh, S.P., Pradhan, D., Sahu, P.K., Kar, J.P., Investigation of morphological and electrical properties of RTA-processed TiO 2 for memristor application. Journal of Sol-Gel Science and Technology, pp.1-16, 3rd September 2020.
  22. Alharbi, A.G., Chowdhury, M.H., Generic and Practical Emulators for the Voltage-Controlled Memristor Models. Memristor Emulator Circuits, pp.55-73, 2nd September 2020.
  23. Alharbi, A.G., Chowdhury, M.H., Generic and Practical Emulators for the Current-Controlled Memristor Models. Memristor Emulator Circuits, pp.19-35, 2nd September 2020.
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