Sizing of the multilayer perceptron via modular networks

A fast method for sizing the multilayer perceptron is proposed. The principal assumption is that a modular network with the same theoretical pattern storage as the multilayer perceptron has the same training error. This assumption is analyzed for the case of random patterns. Using several benchmark datasets, the validity of the approach is demonstrated.