MATEC Web Conf.
Volume 132, 2017XIII International Scientific-Technical Conference “Dynamic of Technical Systems” (DTS-2017)
|Number of page(s)||4|
|Section||Fundamental methods of system analysis, modeling and optimization of dynamic systems|
|Published online||31 October 2017|
Modelling of Fuzzy Expert Information in the Problem of a Machine Technological Adjustment
1 Department of Quality Management, Don State Technical University, Rostov-on-Don, 346500, Russia
2 Department of Economics and Management in Engineering, Don State Technical University, Rostov-on-Don, 346500, Russia
3 Department of Applied Mathematics, Don State Technical University, Rostov-on-Don, 346500, Russia
* Corresponding author: firstname.lastname@example.org
The paper considers the problem of stating fuzzy expert information for intelligent systems of decision support. The application of such systems is worthwhile when complex agricultural machines operate under the changing field conditions, characterized by fuzziness of information concerning external environment and parameters of a machine. One of the important stages of making expert systems for agricultural machines is modelling of fuzzy information regarding input and output parameters of the system and also their interrelations. The paper presents the problem solution of linguistic representation of the adjustable parameters of a combine harvester: speed of the combine movement, rotational speed of the threshing drum, and rotational speed of the separator fan. These parameters are most important for providing qualitative indices of technological process in combine harvesting, and they are characterized by efficiency of control and accessibility of updating. As a result of the analysis, linguistic variable, basic and extended term-sets have been determined. On the basis of the information, obtained from four experts, membership functions have been constructed with the use of standard functions of triangle and trapezoidal types. The indices of consistency of the expert models have been calculated, and the optimal models have been selected on the basis of their analysis.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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