A memristive multilayer cellular neural network with applications to image processing
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 7469884 |
| Pages (from-to) | 1889-1901 |
| Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 28 |
| Issue number | 8 |
| Online published | 13 May 2016 |
| Publication status | Published - Aug 2017 |
Link(s)
Abstract
The memristor has been extensively studied in electrical engineering and biological sciences as a means to compactly implement the synaptic function in neural networks. The cellular neural network (CNN) is one of the most implementable artificial neural network models and capable of massively parallel
analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the
synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through
simulations.
analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the
synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through
simulations.
Research Area(s)
- Image processing, memristors, multilayer cellular neural networks (CNNs), stability, synaptic circuits
Citation Format(s)
A memristive multilayer cellular neural network with applications to image processing. / Hu, Xiaofang; Feng, Gang; Duan, Shukai et al.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 8, 7469884, 08.2017, p. 1889-1901.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, No. 8, 7469884, 08.2017, p. 1889-1901.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
