Volume 11, Issue 2 (2009)
JAST 2009, 11(2): 147-160 |
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Hassan-Beygi S R, Ghobadian B, Amiri Chayjan R, Kianmehr M H. Prediction of Power Tiller Noise Levels Using a Back
Propagation Algorithm. JAST 2009; 11 (2) :147-160
URL: http://jast.modares.ac.ir/article-23-10771-en.html
URL: http://jast.modares.ac.ir/article-23-10771-en.html
1- Department of Agricultural Technical Engineering, Aboureihan Campus, University of Tehran, Pakdasht,
Islamic Republic of Iran.
2- Department of Mechanics of Agricultural Machinery, University of Tarbiat Modares, Tehran, Islamic Republic of Iran.
3- Department of Agricultural Machinery Engineering, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
2- Department of Mechanics of Agricultural Machinery, University of Tarbiat Modares, Tehran, Islamic Republic of Iran.
3- Department of Agricultural Machinery Engineering, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
Abstract: (6096 Views)
The use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. The application of artificial neural networks for pre-diction 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 op-eration in transportation conditions for the asphalt, dirt rural roads, and grassland. Sig-nals 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. Arti-ficial 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.
Subject:
Agricultural Machinery
Received: 2010/01/25 | Accepted: 2010/01/25 | Published: 2010/01/25
Received: 2010/01/25 | Accepted: 2010/01/25 | Published: 2010/01/25
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