Journal
NEURAL PROCESSING LETTERS
Volume 47, Issue 2, Pages 601-618Publisher
SPRINGER
DOI: 10.1007/s11063-017-9671-x
Keywords
Memristor; Recurrent neural networks; Stability
Categories
Funding
- Natural Science Foundation of China [61125303]
- Program for Science and Technology in Wuhan of China [2014010101010004]
- Program for Changjiang Scholars and Innovative Research Team in University of China [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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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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