A non-negative artificial neural network configuration for noise predictions

Abstract

Artificial Neural Networks (ANN) provide an accurate relationship between user-defined inputs and outputs for complex problems. Their basic units are called neurons. The computation that takes place in each neuron is a weighted summation of its inputs. These results are then implemented as an input to an activation or transfer function, which in turn defines the final neuron’s output. The transfer function configuration is very important when defining the final purpose of the ANN. There are many standard activation functions like pure linear, tangent sigmoid, logistic sigmoid, among others. In this study, an ANN capable of predicting tire pavement interaction noise (TPIN) is constructed. Its outputs correspond to sound pressure in narrowband. Thus, the main requirement is that the outputs must be positive. Several neuron transfer functions were investigated for both hidden and output layers. Finally, a customized hybrid linear-sigmoid transfer function was selected. The final ANN configuration is able to capture typical TPIN spectral content within 400 Hz–1600 Hz. In addition, the error is significantly reduced if compared to those obtained after implementing standard transfer functions.