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• W2379133119 startingPage "1889" @default.
• W2379133119 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." @default.
• W2379133119 created "2016-06-24" @default.
• W2379133119 creator A5035048973 @default.
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• W2379133119 date "2017-08-01" @default.
• W2379133119 modified "2023-10-17" @default.
• W2379133119 title "A Memristive Multilayer Cellular Neural Network With Applications to Image Processing" @default.
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• W2379133119 doi "https://doi.org/10.1109/tnnls.2016.2552640" @default.
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• W2379133119 hasPublicationYear "2017" @default.
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