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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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