A novel adaptive activation function

This paper deals with an experimental justification of a novel adaptive activation function for feedforward neural networks (FNNs). Simulation results reveal that FNNs with the proposed adaptive activation function present several advantages over traditional neuron-fixed feedforward networks such as much reduced network size, faster learning, and lessened approximation errors. Following the definition of the neuron-adaptive activation function, we conduct experiments with function approximation and financial data simulation, and depict the experimental outcomes that exhibit the advantages of FNN with our neuron-adaptive activation function over traditional FNN with fixed activation function