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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Gang Bao, Yide Zhang and Zhigang Zeng, "Memory Analysis for Memristors and Memristive Recurrent Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 96-105, Jan. 2020. doi: 10.1109/JAS.2019.1911828
Citation: Gang Bao, Yide Zhang and Zhigang Zeng, "Memory Analysis for Memristors and Memristive Recurrent Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 96-105, Jan. 2020. doi: 10.1109/JAS.2019.1911828

Memory Analysis for Memristors and Memristive Recurrent Neural Networks

doi: 10.1109/JAS.2019.1911828
Funds:  The work was supported by the National Natural Science Foundation of China (618760 97, 61673188, 61761130081), the National Key Research and Development Program of China (2016YFB0800402), the Foundation for Innovative Research Groups of Hubei Province of China (2017CFA005), and the Fundamental Research Funds for the Central Universities (2017KFXKJC002)
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  • Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers. Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis, i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors' mathematical models with linear and nonlinear drift. Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.


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  • [1]
    L. Chua, " Memristor-the missing circuit element,” IEEE Trans. Circ. Theory, vol. 18, no. 5, pp. 507–519, 1971. doi: 10.1109/TCT.1971.1083337
    L. O. Chua and S. M. Kang, " Memristive devices and systems,” Proc. IEEE, vol. 64, no. 2, pp. 209–223, 1976. doi: 10.1109/PROC.1976.10092
    D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, " The missing memristor found,” Nature, vol. 453, no. 7191, pp. 80–83, 2008. doi: 10.1038/nature06932
    L. Chua, " Resistance switching memories are memristors,” App. Phys. A, vol. 102, no. 4, pp. 765–783, 2011.
    M. Itoh and L. O. Chua, " Memristor oscillators,” Int. J. Bifurcation Chaos, vol. 18, no. 11, pp. 3183–3206, 2008. doi: 10.1142/S0218127408022354
    W. Sun, C. Li, and J. Yu, " A memristor based chaotic oscillator,” in Proc. Int. Conf. Com. Circ. Syst. (ICCCAS). IEEE, 2009, pp. 955–957.
    T. Driscoll, Y. Pershin, D. Basov, and M. Di Ventra, " Chaotic memristor,” App. Phys. A, vol. 102, no. 4, pp. 885–889, 2011. doi: 10.1007/s00339-011-6318-z
    B. Linares-Barranco and T. Serrano-Gotarredona, " Memristance can explain spike-time-dependent-plasticity in neural synapses,” Nature Prec., pp. 1–4, 2009.
    G. Snider, " Memristors as synapses in a neural computing architecture,” in Memristor and Memristive Systems Symposium, 2008.
    G. Snider, " Self-organized computation with unreliable, memristive nanodevices,” Nanotechnology, vol. 18, no. 36, pp. 365202, 2007. doi: 10.1088/0957-4484/18/36/365202
    Y. V. Pershin and M. Di Ventra, " Experimental demonstration of associative memory with memristive neural networks,” Neural Netw., vol. 23, no. 7, pp. 881–886, 2010. doi: 10.1016/j.neunet.2010.05.001
    T. Driscoll, H.-T. Kim, B.-G. Chae, B.-J. Kim, Y.-W. Lee, N. M. Jokerst, S. Palit, D. R. Smith, M. Di Ventra, and D. N. Basov, " Memory metamaterials,” Science, vol. 325, no. 5947, pp. 1518–1521, 2009. doi: 10.1126/science.1176580
    P. O. Vontobel, W. Robinett, P. J. Kuekes, D. R. Stewart, J. Straznicky, and R. S. Williams, " Writing to and reading from a nano-scale crossbar memory based on memristors,” Nanotechnology, vol. 20, no. 42, pp. 425204, 2009. doi: 10.1088/0957-4484/20/42/425204
    K. Smagulova, O. Krestinskaya, and A. P. James, " A memristor-based long short term memory circuit,” Analog Integrated Circuits and Signal Processing, vol. 95, no. 3, pp. 467–472, 2018. doi: 10.1007/s10470-018-1180-y
    A. Irmanova and A. P. James, " Neuron inspired data encoding memristive multi-level memory cell,” Analog Integrated Circuits and Signal Processing, vol. 3, pp. 1–6, 2018.
    Y. V. Pershin and M. Di Ventra, " Neuromorphic, digital, and quantum computation with memory circuit elements,” Proc. IEEE, vol. 100, no. 6, pp. 2071–2080, 2012. doi: 10.1109/JPROC.2011.2166369
    W. J.-S. S. J.-W. L. W. WANG Xiao-Ping, SHEN Yi, " Review on memristor and its applications,” Acta Automatica Sinica, vol. 39, no. 8, pp. 1170, 2013.
    Y. V. Pershin and M. Di Ventra, " Spin memristive systems: Spin memory effects in semiconductor spintronics,” Phys. Rev. B, vol. 78, no. 11, pp. 113309, 2008. doi: 10.1103/PhysRevB.78.113309
    I.-S. Yoon, J. S. Choi, Y. S. Kim, S. H. Hong, I. R. Hwang, Y. C. Park, S.-O. Kang, J.-S. Kim, and B. H. Park, " Memristor behaviors of highly oriented anatase TiO2 film sandwiched between top Pt and bottom SrRuO3 electrodes,” App. Phys. Express, vol. 4, no. 4, pp. 1101, 2011.
    F.-Y. Wang, " Memristor for introductory physics,” arXiv Preprint arXiv: 0808.0286, 2008.
    Z. Biolek, D. Biolek, and V. Biolková, " SPICE model of memristor with nonlinear dopant drift,” Radioengineering, vol. 18, no. 2, pp. 210–214, 2009.
    E. Drakakis, S. Yaliraki, and M. Barahona, " Memristors and Bernoulli dynamics,” in Proc. Int. Workshop Cell. Nano. Netw. App. (CNNA). IEEE, 2010, pp. 1–6.
    D. Wang, Z. Hu, X. Yu, and J. Yu, " A PWL model of memristor and its application example,” in Proc. Int. Conf. Com. Circ. Syst. (ICCCAS). IEEE, 2009, pp. 932–934.
    Y. Zhang, X. Zhang, and J. Yu, " Approximated SPICE model for memristor,” in Proc. Int. Conf. Com. Circ. Syst. (ICCCAS). IEEE, 2009, pp. 928–931.
    Z. Biolek, D. Biolek, and V. Biolkova, " Analytical solution of circuits employing voltage- and current-excited memristors,” IEEE Trans. Circ. Syst, vol. 59, no. 11, pp. 2619–2628, 2012. doi: 10.1109/TCSI.2012.2189058
    Z. Li, Y. Tao, A. Abu-Siada, M. A. S. Masoum, Z. Li, Y. Xu, and X. Zhao, " A new vibration testing platform for electronic current transformers,” IEEE Trans. Instrumentation and Measurement, vol. 68, no. 3, pp. 704–712, 2019. doi: 10.1109/TIM.2018.2854939
    B. Bao, F. Feng, W. Dong, and S. Pan, " The voltage-current relationship and equivalent circuit implementation of parallel flux-controlled memristive circuits,” Chinese Phys. B, vol. 6, pp. 101, 2013.
    R. Budhathoki, M. Sah, S. Adhikari, H. Kim, and L. Chua, " Composite behavior of multiple memristor circuits,” IEEE Trans. Circ. Syst. I:Regular Papers, vol. 60, no. 10, pp. 2688–2700, 2013. doi: 10.1109/TCSI.2013.2244320
    H. Kim, M. Sah, C. Yang, S. Cho, and L. Chua, " Memristor emulator for memristor circuit applications,” IEEE Trans. Circ. Syst. I:Regular Papers, vol. 59, no. 10, pp. 2422–2431, 2012. doi: 10.1109/TCSI.2012.2188957
    G. Bao and Z. Zeng, " Stability analysis for memristive recurrent neural network under different external stimulus,” Neural Processing Letters, vol. 47, no. 2, pp. 601–618, 2018.
    L. Wang, Z. Zeng, M.-F. Ge, and J. Hu, " Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays,” Neural Networks, vol. 105, pp. 65–74, 2018. doi: 10.1016/j.neunet.2018.04.014
    L. Wang, T. Dong, and M.-F. Ge, " Finite-time synchronization of memristor chaotic systems and its application in image encryption,” Applied Mathematics and Computation, vol. 347, pp. 293–305, 2019. doi: 10.1016/j.amc.2018.11.017
    Y. Sheng, H. Zhang, and Z. Zeng, " Stabilization of fuzzy memristive neural networks with mixed time delays,” IEEE Trans. Fuzzy Systems, vol. 26, no. 5, pp. 2591–2606, 2017.
    G. Bao, Z. G. Zeng, and Y. J. Shen, " Region stability analysis and tracking control of memristive recurrent neural network,” Neural Networks, vol. 98, no. 2, pp. 51–58, 2018.


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