A Local Supervised Learning Algorithm For Multi-Layer Perceptrons

The back propagation of error in multi-layer perceptrons when used for supervised training is a non-local algorithm in space, that is it needs the knowledge of the network topology. On the other hand, learning rules in biological systems with many hidden units, seem to be local in both space and time. In this work, a local learning algorithm is proposed which makes no distinction between input, hidden and output layers. Simulation results are presented and compared with other well known training algorithms. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)