Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN
This paper presents an approach for the predictive analysis of teletraffic performance indices through derived analytical and regression structures based on Artificial Intelligence. The systematization of synthesis, testing, and verification processes for simulation-modeled teletraffic ICT infrastru...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/60/1/11 |
| _version_ | 1827306329417449472 |
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| author | Ivelina Balabanova Georgi Georgiev |
| author_facet | Ivelina Balabanova Georgi Georgiev |
| author_sort | Ivelina Balabanova |
| collection | DOAJ |
| description | This paper presents an approach for the predictive analysis of teletraffic performance indices through derived analytical and regression structures based on Artificial Intelligence. The systematization of synthesis, testing, and verification processes for simulation-modeled teletraffic ICT infrastructure with queue service organization was carried out. The forecast models for the selected system throughput and system response time indices against the specific complex indicator Service Demand were obtained. Polynomial regression models based on the Coefficient of determination R were achieved. In the course of procedural teletraffic forecasting, we used Feed-Forward Neural Networks (FFNNs), Generalized Regression Neural Networks (GRNNs), and Cascade-Forward Neural Networks. The selection of neural models was performed as the functional minimization of the Mean-Squared Error (MSE) and Mean Absolute Error (MAE). |
| first_indexed | 2024-04-24T18:19:34Z |
| format | Article |
| id | doaj.art-cb5c90f98ccd4c3d98f184be7f3d9d53 |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-04-24T18:19:34Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-cb5c90f98ccd4c3d98f184be7f3d9d532024-03-27T13:36:43ZengMDPI AGEngineering Proceedings2673-45912024-01-016011110.3390/engproc2024060011Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNNIvelina Balabanova0Georgi Georgiev1Department of Communications Equipment and Technologies, Technical University of Gabrovo, 5300 Gabrovo, BulgariaDepartment of Communications Equipment and Technologies, Technical University of Gabrovo, 5300 Gabrovo, BulgariaThis paper presents an approach for the predictive analysis of teletraffic performance indices through derived analytical and regression structures based on Artificial Intelligence. The systematization of synthesis, testing, and verification processes for simulation-modeled teletraffic ICT infrastructure with queue service organization was carried out. The forecast models for the selected system throughput and system response time indices against the specific complex indicator Service Demand were obtained. Polynomial regression models based on the Coefficient of determination R were achieved. In the course of procedural teletraffic forecasting, we used Feed-Forward Neural Networks (FFNNs), Generalized Regression Neural Networks (GRNNs), and Cascade-Forward Neural Networks. The selection of neural models was performed as the functional minimization of the Mean-Squared Error (MSE) and Mean Absolute Error (MAE).https://www.mdpi.com/2673-4591/60/1/11performance indicesteletraffic predictionregression analysisfeed forward |
| spellingShingle | Ivelina Balabanova Georgi Georgiev Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN Engineering Proceedings performance indices teletraffic prediction regression analysis feed forward |
| title | Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN |
| title_full | Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN |
| title_fullStr | Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN |
| title_full_unstemmed | Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN |
| title_short | Forecasting Teletraffic Performance Using Regression Analysis, FNNN, GRNN and CFNN |
| title_sort | forecasting teletraffic performance using regression analysis fnnn grnn and cfnn |
| topic | performance indices teletraffic prediction regression analysis feed forward |
| url | https://www.mdpi.com/2673-4591/60/1/11 |
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