Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands

Problem and goal. The development of mass open online courses contributes to the increasing attention of students to them. At the moment, there are many large services that provide online training, but there are no clearly defined universal requirements for such courses. Also, along with this problem, there is a fairly high level of rejection of the course at various stages due to the loss of motivation to continue training. Methodology. A variant of solving these problems by using adaptive learning technologies on the example of a course on learning artificial neural network technologies was considered. Results. In the process of reviewing the issue, the topics of the online course sections were determined. As a result, a work plan was drafted and the most relevant ways to solve the identified problems were formulated. Conclusion. The developed strategy can help with further elaboration and testing of the designed course and can be applied to any mass open online course.

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Artificial neural network and fuzzy logic controller for GTAW modeling and control

Chinese Journal of Mechanical Engineering ◽

10.3901/cjme.2002.01.053 ◽

2002 ◽

Vol 15 (01) ◽

pp. 53

Author(s):

Xiangdong Gao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Modeling And Control ◽

Artificial Neural ◽

And Control

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Method of identification of technical condition of equipped train equipment

Issues of radio electronics ◽

10.21778/2218-5453-2020-4-48-56 ◽

2020 ◽

pp. 48-56

Author(s):

Y. S. Kucherov ◽

R. V. Dopira ◽

A. A. Shvedun ◽

D. V. Yagolnikov

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Function ◽

Technical Condition ◽

The Neural Network ◽

Multilayer Architecture ◽

Artificial Neural ◽

Network Technologies ◽

Neural Network Technologies ◽

Electric Train

Due to the fact that the equipment of modern electric trains is functionally and technologically complicated, the relevance of creating airborne systems for predictive monitoring of the technical condition of trains to identify their actual and predicted technical condition is increasing. At present, it has not been possible to build automatic on-board systems for predictive monitoring of the technical condition of trains. One of the possible solutions to this problem can be considered the creation of on-board systems, the identification of the technical condition of equipment in which is carried out using neural network technologies. The article proposes a methodology for identifying the technical condition of electric train equipment using artificial neural network technologies, which allows real-time detection of the occurrence and development of malfunctions of electric train equipment with the display of information on the display in the driver’s cab. Taking into account the specifics of the problem being solved, the choice of a multilayer architecture of a direct distribution neural network is justified. All layers of the neural network are completely interconnected, while the number of neurons of the input and output layers of the network is determined, equal to the number of controlled parameters of the technical condition of the electric train and the number of its possible technical conditions, respectively. As a function of activation of network neurons, a logistic function was selected. A heuristic approach is used to train an artificial neural network.

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Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽

10.3390/s21103373 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3373

Author(s):

Ludek Cicmanec

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Regression Function ◽

Large Data ◽

Soil Strength ◽

Data Sets ◽

Bearing Strength ◽

Artificial Neural ◽

Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

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