Supervised learning for multilayered neural network with non-monotonic activation functions

We describe the performance of multilayer neural network with hidden units of nonmonotonic activation functions. Our previous work has shown that the network was effective in improving two difficulties: a convergence to local minima and a slow learning speed for the exclusive-OR and the binary addition problems. The purpose of this paper is to evaluate the performance of the proposed network for more complicated tasks, that is, N-bits parity tasks and two spirals task. Furthermore, we evaluate the generalization performance of the network for an acoustic diagnosis. The results show that the networks are effective for the tasks and have the same generalization performance as the networks with the sigmoidal activation functions.