Modelling of Fuzzy Expert Information in the Problem of a Machine Technological Adjustment

Abstract

Abstract. 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.

Development and analysis of fuzzy expert data for technological adjustment of a grain harvester header

“…To choose the optimal model as a criterion it is expedient to use indices of pair consistency, indices of pair consistency, indices of fuzziness, indices of overall and pair consistency [14,16].…”

“…where l=1, 2,…, m ˗ term number, i=1, 2,…, k ˗ expert number, µ il (x) ˗ MF, which was assigned by i-th expert for l-th term. Fuzziness index l ij d between the models of two experts, i-th and j-th, in terms of l-th term is determined as a linear distance (Hemming) between fuzzy sets with MF µ il (x) and µ jl (x) [13], and the index of pair consistency between the models -as a magnitude l ij k [14,16]:…”

“…Arithmetic mean or weighted average of the parameters estimates is used, and in the function of weighting coefficients Fishburn numbers are applied. The fundamental principle for ranking may be the consistency degree of the data, obtained from each expert, with the others [16]. Another criterion may be the experts' qualification, which, in a number of cases, is not insignificant, since such an approach makes it possible to give the priority to well-trained experts and, in addition, to take into account the input of the others.…”

Development and Experimental Analysis of a Fuzzy Grey Control System on Rapeseed Cleaning Loss

“…The feed amount and loss rate of the combine in field work was researched by Miosz [14], and a working speed control model of the combine harvester was established. A fuzzy expert system in an intelligent decision support system was examined, and an expert system that modeled walking speed, threshing cylinder speed, and fan speed was proposed and employed by Dimitrov et al [15]. Chaab et al [16] conducted modeling and evaluation of header loss on a wheat combine harvester.…”