Prediction of Power Tiller Noise Levels Using a Back Propagation Algorithm

The use of neural networks methodology is not as common in the investigation and prediction noise as statistical analysis. The application of artificial neural networks for prediction of power tiller noise is set out in the present paper. The sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. During measurement and recording of the sound pressure signals of the power tiller, the engine speeds and gear ratios were varied to cover the most normal range of the power tiller operation in transportation conditions for the asphalt, dirt rural roads, and grassland. Signals recorded in the time domain were converted to the frequency domain with the help of a specially developed Fast Fourier Transform (FFT) program. The narrow band signals were further processed to obtain overall sound pressure levels in A-weighting. Altogether, 48 patterns were generated for training and evaluation of artificial neural networks. Artificial neural networks were designed based on three neurons in the input layer and one neuron in the output layer. The results showed that multi layer perceptron networks with a training algorithm of back propagation were best for accurate prediction of power tiller overall noise. The minimum RMSE and R2 for the four-layer perceptron network with a sigmoid activation function, Extended Delta-Bar-Delta (Ext. DBD) learning rule with three neurons in the first hidden layer and two neurons in the second hidden layer, were 0.0198 and 0.992, respectively.