In this study the wavelet neural network (WNN) and artificial neural network (ANN) were used to simulate barley breakage percentage in combine harvester. The models have been trained using the same data conditions. Air temperature, thresher cylinder speed, distance between thresher cylinder and concave (back and forth) and the percentage of barely moisture were as the input variables. The results showed that the wavelet network (WNN, RASP 1) with 90.2% correlation coefficient for barely breakage would be an appropriate substitute for artificial neural network with 88% correlation coefficient. The result of sensitivity analysis showed that all input variables had a significant effect on barely breakage. Speed of thresher cylinder had the most effect and the degree of air temperature had the least effect on barely breakage.
Mazloumzadeh, S. M., Alavi, N., & Nouri, M. (2008). Comparison of Artificial Neural and Wavelet Neural Networks for Prediction of Barley Breakage in Combine Harvester. Agriculture (Not Published), 10(2), 181-195.
MLA
Seyed Meisam Mazloumzadeh; Naser Alavi; Mojtaba Nouri. "Comparison of Artificial Neural and Wavelet Neural Networks for Prediction of Barley Breakage in Combine Harvester", Agriculture (Not Published), 10, 2, 2008, 181-195.
HARVARD
Mazloumzadeh, S. M., Alavi, N., Nouri, M. (2008). 'Comparison of Artificial Neural and Wavelet Neural Networks for Prediction of Barley Breakage in Combine Harvester', Agriculture (Not Published), 10(2), pp. 181-195.
VANCOUVER
Mazloumzadeh, S. M., Alavi, N., Nouri, M. Comparison of Artificial Neural and Wavelet Neural Networks for Prediction of Barley Breakage in Combine Harvester. Agriculture (Not Published), 2008; 10(2): 181-195.
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