Fast training of backpropagation networks employing threshold logic transform

A backpropagation network model employing the threshold logic transform (TLT) for faster training is proposed. TLT, a simple mathematical transformation, is inserted between the input layer and the hidden layer to speed up extraction of complex features of input. When comparing the conventional method versus adding TLT the study of three classification tasks revealed that with TLT the convergence speed is 5 to 20 times faster. The rate of convergence is also much greater. The conventional method is 33.3%, whereas, with TLT it is 99.3%. Furthermore, analyses of the weighted sum of inputs reveal that the hidden units of the proposed networks effectively extract the global features of input.