Abstract
Memristor is the fourth missing element. This paper discusses dynmacis memristive recurrent neural network with memristors as synapses. Firstly, it analyzes variation property of memristance under different external inputs with memristor simulation model. It concludes that memristance will be stable at one value if the direction of voltage is not changed and be varying periodically under periodically variable voltage. Next, it presents the memristive recurrent neural network model and gives local attractive region, one sufficient condition for memristive recurrent neural network under periodic voltage source. At last, an illustrative example is given for verifying our result.
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Acknowledgements
The authors would like to thank the associate editor and the reviewers for their detailed comments and valuable suggestions which considerably improved the presentation of the paper. The work is supported by the Natural Science Foundation of China under Grant 61125303, the Program for Science and Technology in Wuhan of China under Grant 2014010101010004, the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant IRT1245, China Three Gorges University Science Foundation KJ2013B020, Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control Program 2013KJX12, Hubei science and technology support program: 2015BAA106, Yichang natural science research and application project A15302a11.
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Bao, G., Zeng, Z. Stability Analysis for Memristive Recurrent Neural Network Under Different External Stimulus. Neural Process Lett 47, 601–618 (2018). https://doi.org/10.1007/s11063-017-9671-x
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DOI: https://doi.org/10.1007/s11063-017-9671-x