Digital Networks and Computer Systems
Digital Networks and Computer Systems
Constructive neural networks to predict breast cancer outcome by using gene expression profiles
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
A novel neural network parallel adder
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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A comparison study was carried out between feedforward neural networks composed of binary linear threshold units and digital circuits. These networks were generated by the regular partitioning algorithm and a modified Quine-McCluskey algorithm, respectively. The size of both types of networks and their generalisation properties are compared as a function of the nearest-neighbour correlation in the binary input sets. The ratio of the number of components required by digital circuits and the number of neurons grows linearly for the input sets considered. The considered neural networks do not outperform digital circuits with respect to generalisation. Sensitivity analysis leads to a preference for digital circuits, especially for increasing number of inputs. In the case of analog input sets, hybrid networks of binary neurons and logic gates are of interest.