An update function that speeds up backpropagation learning

We consider a modification of the backpropagation (BP) learning algorithm in which a linear function, directly proportional to the deviation between target values and actual values at the output, is propagated backwards instead of the original nonlinear function. The new algorithm is tested on the odd/even parity function for orders between 4 and 7 and on high- (180) dimensional data derived from NMR spectroscopy of animal tumours. Results suggest that using the linear function, the network converges faster and is more likely to escape from local minima than when the original BP is used