Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Color image associative memory on a class of Cohen-Grossberg networks
Pattern Recognition
Design of associative memory for gray-scale images by multilayer Hopfield neural networks
ICC'06 Proceedings of the 10th WSEAS international conference on Circuits
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In contrast to the usual types of neural networks which utilize two states for each neuron, a class of synchronous discrete-time neural networks with multilevel threshold neurons is developed. A qualitative analysis and a synthesis procedure for the class of neural networks considered constitute the principal contributions of this paper. The applicability of the present class of neural networks is demonstrated by means of a gray level image processing example, where each neuron can assume one of sixteen values. When compared to the usual neural networks with two state neurons, networks which are endowed with multilevel neurons will, in general, for a given application, require fewer neurons and thus fewer interconnections. This is an important consideration in VLSI implementation