Abstract
Cellular neural networks (CNNs) are an efficient tool for image analysis and pattern recognition. Based on elementary cells connected to neighboring units, they are easy to install in hardware, carrying out massively parallel processes. This brief presents a new model of CNN with memory devices, which enhances further CNN performance. By introducing a memristive element in basic cells, we carry out different experiments, allowing the analysis of the functions traditionally carried out by the standard CNN. Without modifying the templates considered by the scientific literature, this simple variation originates a significant improvement in ∼30 % of performances in pattern recognition and image processing. These progresses were experimentally calculated on the time the system requires to reach a fixed point. Moreover, the different role that each parameter has in the developed method was also analyzed to better understand the complex processing ability of these systems.
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