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Erschienen in: Neural Computing and Applications 2/2014

01.08.2014 | Original Article

Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing

verfasst von: Shukai Duan, Xiaofang Hu, Lidan Wang, Shiyong Gao, Chuandong Li

Erschienen in: Neural Computing and Applications | Ausgabe 2/2014

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Abstract

Cellular neural network (CNN) has been acted as a high-speed parallel analog signal processor gradually. However, recently, since the decrease in the size of transistor is going to approach the utmost, the transistor-based integrated circuit technology hits a bottleneck. As a result, the advantage of very large scale integration implementation of CNN becomes hard to really present, and further development of this era faces severe challenges unavoidably. In this study, two types of memristor-based cellular neural networks have been proposed. One type uses a memristor to replace the linear resistor in a conventional CNN cell circuit. And the other places a resonant tunneling diode (RTD) in this position and uses memristive synaptic connections to structure a hybrid memristor RTD CNN model. The excellent performances of the proposed CNNs are verified by conventional means of, for instance, stability analysis and efficient applications in image processing. Since both the memristor and the resonant tunneling diode are nanoscale, the size of the network circuits can be greatly reduced, and the integration density of the system will be significantly improved.

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Metadaten
Titel
Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing
verfasst von
Shukai Duan
Xiaofang Hu
Lidan Wang
Shiyong Gao
Chuandong Li
Publikationsdatum
01.08.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1484-x

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