We study the equilibrium properties of the real weights linear percept
ron within the replica formalism Framework, focusing on the effects of
the normalization of the weights on the learning and generalization c
apabilities of the network. We also investigate the effects of static
noise corrupting the training data and of dynamical noise acting on th
e weights during the training stage.