Effect of Activation Function Symmetry on Training of SFFANNs with RPROP Algorithm

On ten learning tasks (5 function approximation and 5 real life regression problems), we compare the effciency and efficacy of using asymmetric or anti-symmetric activation functions in sigmoidal feedforward artificial neural network training and usage. The result obtained in the experiment allows us to conclude that for networks trained using the improved variant of the resilient backpropagation algorithm, the usage of asymmetric activation functions like the logistic or the log-sigmoid function should be preferred as compared to anti-symmetric function such that the two functions have the same derivative.