Algorithm to Adjust Fuzzy Inference System of Mamdani Type

Golosovskiy, Mikhail; Bogomolov, A. V.; Terebov, D.S.; Евтушенко, Е. В.

doi:10.14529/mmph180303

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…The creation and subsequent practical application of FIS is carried out in several stages, each of which is carried out on the basis of the use of known fuzzy logic methods given in various sources. for example [15][16][17][18][19]. The process of inference itself includes the following steps, shown in figure 1:  introduction of fuzziness (fuzzification based on membership functions);  fuzzy inference (based on the knowledge base or the rule base);  composition and reduction to clarity (defuzzification using membership functions).…”

Section: Methodsmentioning

confidence: 99%

“…Its application will allow taking into account and processing the uncertainty, incompleteness of the initial data, as well as using the experience of experts in the field of energy. On the basis of such models, fuzzy inference systems (FIS) are created that implement the processes of obtaining fuzzy estimates about the current state of the system under study [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Model of fuzzy inference for energy efficiency evaluation at industrial enterprises

Biryulin

Kudelina

Pykhtin

et al. 2022

J. Phys.: Conf. Ser.

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At the moment, with the developing industrialization of society, as well as the growing demand and consumption of all types of end products, industry is increasing the use of energy resources to create capacities corresponding to the current time. The main industrial source of energy in the 21st century is electricity. In industries and enterprises, electricity is used in large amount, and that is why the main and urgent task to reduce the cost, increase the competitive ability of the organization and the growth of its microeconomics is the ability to reduce electricity consumption, and not by reducing production lines, but due to the correct and modern technology of its use. To solve this problem, many factors, which are expressed in quantitative and qualitative data, should be taken into consideration. But it is complicated by the fact that most often these data groups have incompleteness and uncertainty of the used information. This may be due to outdated equipment, lack of necessary technological equipment, or simply a human factor. In this regard, there is a not objective and not quite correct model and assessment of electricity usage by the enterprise. To get out of this situation with the benefit of industry, it is possible to use the mathematical apparatus of fuzzy logic. The results obtained using this method can be considered objective. Based on reasonable estimates of the impact of various factors on energy consumption and energy efficiency of the enterprise system, it is possible to develop methods for its improvement, increasing energy efficiency, reducing waste, which leads to an increase in the competitiveness of the enterprise by reducing the cost of the final product and reducing the cost for the consumer.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model of fuzzy inference for energy efficiency evaluation at industrial enterprises

Biryulin

Kudelina

Pykhtin

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…[2]. From the analysis of the literature [1][2][3][4][5][6][7][8], we can conclude that a perspective way of creation of mathematical models of IDSS for operator of the unmanned aerial vehicle (UAV) is to develop a fuzzy logic-linguistic models based on fuzzy inference systems Mamdani-type [9].…”

Section: Introductionmentioning

confidence: 99%