Modeling of tractive performance of Massey Ferguson tractor (MF 285) in different field conditions using artificial neural networks

Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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NCAMEM10_178

تاریخ نمایه سازی: 6 اسفند 1395

Abstract:

Implementation of tractors in agriculture is Substantial as a power supply. Therefore, performance model for developing parameters of tractors and implements are major for farm machinery, operators and manufacturers alike. The objective of this study was to assess the predictive capability of several configurations of ANNs for performance evaluating of tractor in parameters of drawbar power, rolling resistance and tractive efficiency. A conventional tillage system which included a moldboard plow with three furrows was used for collecting data from MF285 Massey Ferguson tractor. To predict performance parameters, ANN models with back-propagation algorithm were developed using a MATLAB software with different topologies and training algorithms. For drawbar power. The best result was obtained by the ANN with 6-7-1 topology and Bayesian regulation training algorithm with R2 of 0.995 and MSE of 0.00024. The obtained result showed that the 6-7-1structred ANN with Levenberg-Marquardt training algorithm represents a good prediction of TE with R2 equal to 0.989 and MSE of 0.001327. The obtained results confirmed that the neural network can be able to learn the relationships between the input variables and performance parameters of tractor, very well.

Authors

Salim Almaliki

PhD student, Department of Agricultural Machinery, University of Basrah, Basrah, Iraq. - Faculty member, Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Reza Alimardani

Faculty member, Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Mahmoud Omid

Faculty member, Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

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