On the feasibility of solving regression learning tasks with FFANN using non-sigmoidal activation functions

In this paper, parametrized non-sigmoidal, continuous and bounded function(s) are proposed as the activation function at the hidden nodes of a feedforward artificial neural networks (FFANN). On a set of 5 regression (benchmark) tasks that correspond to real-life learning problems, the effect of the usage of the parametrized function as the activation function at the hidden layer nodes, on the efficiency and efficacy of training the FFANN is studied. It is observed that on the given set of problems, one of the parameterized activation function (with a particular parameter value), gives statistically meaningful results (lower minima of the error functional during training) as compared to the standard log-sigmoid activation function in 4 cases while in the fifth problem, the two activations are found to be statistically equivalent.