scholarly journals Neural network architecture for outputting solutions in dangerous situations at a complex technological facility

2021 ◽  
Vol 16 (95) ◽  
pp. 99-107
Author(s):  
Igor N. Glukhikh ◽  
◽  
Yuri E. Karyakin ◽  
Dmitry I. Glukhikh ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Emergency Situation ◽  
Complex Object ◽  
Case Based Reasoning ◽  
Neural Network Architecture ◽  
Complex Objects ◽  
Knowledge Based ◽  
Industrial Mining

Preventing and neutralize dangerous situations tasks are relevant in the operation of complex technological objects. Complex technological objects present, in particular, in the support city systems (heat, water, energy, gas supply systems), at large industrial, mining, or processing enterprises. The development of dangerous situations at such facilities can lead to undesirable or even catastrophic consequences. The decision-making process to neutralize (prevent) an emerging dangerous situation is aimed at finding an action program that should transfer the current emergency situation into a target, standard situation. The article examines the possibility of implementing a case-based reasoning method for retrieving a solution using a neural network in order to prevent and neutralize dangerous situations at a complex technological object of city infrastructure. The authors consider the situation as a set of elements states of a complex object and the relationships between elements. To solve the tasks, the work examines two neural network architectures: a model that builds upon the multilayer perceptron and the "comparator - adder" architecture.Experiments have shown that the proposed neural network architecture "comparator - adder" showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The obtained result continues the well-known research in the integration of machine learning methods and methods of knowledge-based systems field. It serves as the basis for the further development of decision inference hybrid models for intelligent control of complex objects.

scholarly journals Case-Based Reasoning with an Artificial Neural Network for Decision Support in Situations at Complex Technological Objects of Urban Infrastructure

2021 ◽  
Vol 4 (4) ◽  
pp. 73
Author(s):  
Igor Glukhikh ◽  
Dmitry Glukhikh
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Environmental Influence ◽  
Case Based Reasoning ◽  
Technological Object ◽  
Technological Facility ◽  
High Level ◽  
Case Based

The article considers the tasks of intellectual support for decision support in relation to a complex technological object. The relevance is determined by a high level of responsibility, together with a variety of possible situations at a complex technological facility. The authors consider case-based reasoning (CBR) as a method for decision support. For a complex technological object, the problem defined is the uniqueness of the situations, which is determined by a variety of elements and the possible environmental influence. This problem complicates the implementation of CBR, especially the stages of comparing situations and a further selection of the most similar situation from the database. As a solution to this problem, the authors consider the use of neural networks. The work examines two neural network architectures. The first part of the research presents a neural network model that builds upon the multilayer perceptron. The second part considers the “Comparator-Adder” architecture. Experiments have shown that the proposed neural network architecture “Comparator-Adder” showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

Sign in / Sign up

Export Citation Format

Share Document