A new learning rule for multilayer neural net

The method of generalized projections is applied to the multilayer feedforward neural net problem to derive a learning algorithm. This learning rule is called the projection-method learning rule (PMLR). The authors apply the PMLR to a well-known pattern recognition problem, which cannot be solved by a linear discriminant scheme. The PMLR is compared with the error backpropagation learning rule (BPLR) and is shown to converge faster than the latter for the problems considered. As the degree of nonlinearity of the neuron activation function increases, the PMLR becomes even more superior to the BPLR. >