Abstract
In this paper, based on classical HP memristor we present an expanded model that fully considers the influence of R on We demonstrate the hysteresis effect of the expanded model, and then make a comparison with the HP model under certain voltage load. Simulations show the Expanded HP memristor is a good candidate for memristive neural networks.
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References
Chua, L.O.: Memristor - The Missing Circuit Element. IEEE Transactions on Circuit Theory 18, 507–519 (1971)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453, 80–83 (2008)
Rozenberg, M.J., Inoue, I.H., Sánchez, M.J.: Nonvolatile Memory with Multilevel Switching: A Basic Model. Physical Review Letters 92, 178302 (2004)
Jo, S.H., Lu, W.: CMOS Compatible Nanoscale Nonvolatile Resistance Switching Memory. Nano Letters 8, 392–397 (2008)
Ho, Y., Huang, G.M., Li, P.: Nonvolatile Memristor Memory: Device Characteristics and Design Implications. In: 2009 IEEE/ACM International Conference on Computer-Aided Design Digest of Technical Papers, pp. 485–490. ACM, New York (2009)
Biolek, Z., Biolek, D., Biolková, V.: SPICE Model of Memristor with Nonlinear Dopant Drift. Radioengineering 18, 210–214 (2009)
Snider, G.: Self-organized computation with unreliable, memristive nanodevices. Nanotechnology 18, 365202 (2007)
Biolek, D., Biolek, Z., Biolkova, V.: SPICE modelling of memcapacitor. Electronics letters 46, 520–522 (2010)
Pershin, Y.V., Ventra, M.D.: Practical Approach to Programmable Analog Circuits With Memristors. IEEE Transaction on Circuits and Systems 57, 1857–1864 (2010)
Batas, D., Fiedler, H.: A Memristor SPICE Implementation and a New Approach for Magnetic Flux-Controlled Memristor Modeling. IEEE Transactions on Nanotechnology 10, 250–255 (2011)
Lin, Y.J., Hou, C.L., Su, T.J.: Cellular Neural Networks for Noise Cancellation of Gray Image Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization. In: 2009 International Conference on New Trends in Information and Service Science, pp. 613–617. IEEE, New York (2009)
Pershin, Y.V., Ventra, M.D.: Experimental demonstration of associative memory with memristive neural networks. Neural Networks 23, 881–886 (2010)
Snider, G.: Instar and outstar learning with memristive nanodevices. Nanotechnology 22, 015201 (2011)
Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., Lu, W.: Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Letters 10, 1297–1301 (2010)
Chua, L.O., Kang, S.M.: Memristive Devices and Systems. Proceedings of the IEEE 64, 209–223 (1976)
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Dai, Y., Li, C. (2012). An Expanded HP Memristor Model for Memristive Neural Network. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_76
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DOI: https://doi.org/10.1007/978-3-642-34500-5_76
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