Generalization capability of one and two hidden layers

We present an experimental comparison of the generalization capability of one and two hidden layers multilayer feedforward neural networks. We used sixteen different real world problems in order to measure the generalization of both architectures. For each problem and architecture we carefully selected, by a trial and error procedure, the minimal network which solves the problem. Several runs with different initial conditions were obtained in order to get an average performance with an error. According to our results, the generalization capability of a one hidden layer network is better than the one of two hidden layers network. Furthermore, two hidden layer networks are more prone to fall into bad local minimum