ANN Activation Function Comparative Study for Sinusoidal Data

Abstract

An artificial neural network (ANN) has neurons that are interconnected. Inside the neuron, there is an activation function that determines the value issued by the neuron. There are several types of activation functions. The choice of activation function will determine the result of an ANN. This paper describes the performance comparison of several activation functions used in an ANN in processing sinusoidal signals. There are three types of activation functions being compared, those are Sigmoid, Tansig, and ReLU. The sinusoidal dataset has come from simulation data of the PMSM FOC control process. The results indicated that the Tansig activation function is the best choice for sinusoid data.