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Cellular Neural Networks Seminar Presentation (Read 427 times)


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Cellular Neural Networks
Cellular Neural Networks (CNN) is one of the most widely used computing paradigms due to its space variance and high processing speed. There are numerous applications of CNNs and image processing, signal processing, solving partial differential equations , analyzing 3D surfaces are some of those. Cellular Neural Networks (CNN) consists of N-dimensional regular non-linear array of elements called cells. The cells are basically multiple input-single output processors. The output/state of a cell at any time depends on the input, output of neighboring cells and of course the initial state of the network. The main characteristic of CNN is the locality of the connections which differentiates it from other neural networks. In CNN each cell can exchange information only with its neighboring cells. The communication between indirectly connected cells takes place with propagation effects of the dynamics of CNNs. The CNN system can operate in continuous (CT-CNN) as well as discrete time (DT-CNN).