Abstract
The paper describes a binary neural network architecture and its performance in pattern classification. The network is called binary because its inputs are binary and its main components are composed of binary neurons. Apart from the usual input and output layers, the network has two 'hidden' layers, called code layer and linear plane, connected in a feedforward structure. The weights of these feedforward connections are also binary. The performance of the network is demonstrated through binary pattern classification experiments. Comparisons with many one- and two-hidden-layer backpropagation networks are included. The proposed network shows superior performance in all the cases that have been studied. >
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