CLASSIFICATION OF GLASS TYPE BASED ON NEURO-FUZZY MODEL
Authors:
1.
Kazan State Energy University
2.
Kazan State Energy University
3.
Kazan State Energy University
Type:
Article
eLocation:
Status:
Published
Received:
05.08.2025
Accepted:
07.08.2025
Published:
07.08.2025
Language:
Russian
Keywords:
FUZZY NEURAL NETWORK, NEURO-FUZZY MODEL, MODELING, FUZZY KNOWLEDGE BASE, GLASS TYPE, NEURO-FUZZY CLASSIFICATION
Abstract (English):
This article is devoted to the solution of the problem of constructing a neuro-fuzzy model for classifying the type of glass. The solution of this problem is relevant in criminological investigations, in medicine, the window industry, the automotive industry and other subject areas. To solve it, the feasibility of constructing and using a neuro-fuzzy model is substantiated. To build the model, it was necessary to select and prepare a data set characterizing various types of glass, as well as select and use the tool environment of neuro-fuzzy modeling. To solve the problem, a search for data sets was performed in the UCI repository and the "Glass identification" set was found, designed to recognize the following types of glass: heat-polished glass of buildings, ordinary glass of buildings, heat-polished glass of cars, glass containers, glass for dishes, glass of car headlights. The data set for analysis was prepared on the basis of the Deductor analytical platform. For this, a scenario was developed that provides for the following steps: loading data, setting up a data set, performing correlation analysis, assessing the quality of data, editing outliers and extreme values, exporting data. At the data setup stage, the data types (integer or real) and types (discrete or continuous) were set for the corresponding input and output columns. Correlation analysis allowed us to identify the degrees of influence of each input column on the output column and select informative features for analysis. The data quality assessment stages, editing of outliers and extreme values were also implemented. After completing the specified procedures, the data were exported to a text file for further analysis and building a neuro-fuzzy model. The final data sample for analysis included 214 rows, 4 input columns (Na, Mg, Al, Ba) and 1 output (Type of glass) with 6 classes of glass type. Based on the prepared data, the fuzzy neural network was trained in the Neuro-Fuzzy System for Forming Fuzzy Models for Assessing the Discrete State of Objects software package. The model construction time was 6 minutes and 42 seconds. During this time, 6 full cycles of fuzzy neural network training were implemented. In each cycle, the genetic algorithm adjusted the values of 20 parameters of the membership functions. During training, it was possible to achieve classification accuracy of 93.62% on the training data sample and 92.21% on the test sample. This indicates the adequacy of the constructed model and the possibility of its effective practical use.
This article is devoted to the solution of the problem of constructing a neuro-fuzzy model for classifying the type of glass. The solution of this problem is relevant in criminological investigations, in medicine, the window industry, the automotive industry and other subject areas. To solve it, the feasibility of constructing and using a neuro-fuzzy model is substantiated. To build the model, it was necessary to select and prepare a data set characterizing various types of glass, as well as select and use the tool environment of neuro-fuzzy modeling. To solve the problem, a search for data sets was performed in the UCI repository and the "Glass identification" set was found, designed to recognize the following types of glass: heat-polished glass of buildings, ordinary glass of buildings, heat-polished glass of cars, glass containers, glass for dishes, glass of car headlights. The data set for analysis was prepared on the basis of the Deductor analytical platform. For this, a scenario was developed that provides for the following steps: loading data, setting up a data set, performing correlation analysis, assessing the quality of data, editing outliers and extreme values, exporting data. At the data setup stage, the data types (integer or real) and types (discrete or continuous) were set for the corresponding input and output columns. Correlation analysis allowed us to identify the degrees of influence of each input column on the output column and select informative features for analysis. The data quality assessment stages, editing of outliers and extreme values were also implemented. After completing the specified procedures, the data were exported to a text file for further analysis and building a neuro-fuzzy model. The final data sample for analysis included 214 rows, 4 input columns (Na, Mg, Al, Ba) and 1 output (Type of glass) with 6 classes of glass type. Based on the prepared data, the fuzzy neural network was trained in the Neuro-Fuzzy System for Forming Fuzzy Models for Assessing the Discrete State of Objects software package. The model construction time was 6 minutes and 42 seconds. During this time, 6 full cycles of fuzzy neural network training were implemented. In each cycle, the genetic algorithm adjusted the values of 20 parameters of the membership functions. During training, it was possible to achieve classification accuracy of 93.62% on the training data sample and 92.21% on the test sample. This indicates the adequacy of the constructed model and the possibility of its effective practical use.
Keywords:
FUZZY NEURAL NETWORK, NEURO-FUZZY MODEL, MODELING, FUZZY KNOWLEDGE BASE, GLASS TYPE, NEURO-FUZZY CLASSIFICATION
FUZZY NEURAL NETWORK, NEURO-FUZZY MODEL, MODELING, FUZZY KNOWLEDGE BASE, GLASS TYPE, NEURO-FUZZY CLASSIFICATION
