Multiple channel crosstalk removal using limited connectivity neural networksTools Craven, Michael P., Curtis, K. Mervyn and Hayes-Gill, Barrie R. (1996) Multiple channel crosstalk removal using limited connectivity neural networks. In: 3rd IEEE International Conference on Electronics, Circuits, and Systems (ICECS 96), 13-16 October 1996, Rhodes, Greece. Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/ICECS.1996.584614
AbstractLimited connectivity neural network architectures are investigated for the removal of crosstalk in systems using mutually overlapping sub-channels for the communication of multiple signals, either analogue or digital. The crosstalk error is modelled such that a fixed proportion of the signals in adjacent channels is added to the main signal. Different types of neural networks, trained using gradient descent algorithms, are tested as to their suitability for reducing the errors caused by a combination of crosstalk and additional gaussian noise. In particular we propose a single layer limited connectivity neural network since it promises to be the most easily implemented in hardware. A variable gain neuron structure is described which can be used for both analogue and digital data.
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