An adaptive activation function for multilayer feedforward neural networks

Abstract

The aim of this paper is to propose a new adaptive activation function for multilayer feedforward neural networks. Based upon the backpropagation (BP) algorithm, an effective learning method is derived to adjust the free parameters in the activation function as well as the connected weights between neurons. Its performance is demonstrated by the N-parity and two-spiral problems. The simulation results showed that the proposed method is more suitable to the pattern classification problems and its learning speed is much faster than that of traditional networks with fixed activation function.