Models of self-organizing artificial neural networks to identify stationary industrial sources of air pollution
| Autor: | S. P. Dudarov |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Self-organizing map
Learning vector quantization Artificial neural network Computer science Competitive learning computer.software_genre Computational Mathematics Adaptive resonance theory Modeling and Simulation Unsupervised learning Data mining Types of artificial neural networks Cluster analysis computer |
| Zdroj: | Mathematical Models and Computer Simulations. 9:481-488 |
| ISSN: | 2070-0490 2070-0482 |
| DOI: | 10.1134/s2070048217040032 |
| Popis: | A problem of identifying one particular or a few possible pollution sources that are responsible for the deterioration of the air quality as a result of exceeding the standards of the maximum permissible emissions is considered. A model problem for a group of spatially divided stationary permanent industrial sources is solved. A statement identifying the problem and a method to solve it using two architectures of artificial neural networks, Kohonen’s networks for learning vector quantization with fixed and adaptive structures, as well as adaptive resonance theory network for analog inputs (ART-2), are presented. The method consists of clustering the data provided by self-learning algorithms (unsupervised learning). Estimation equations are given and operation algorithms of Kohonen’s and adaptive resonance theory networks at different life cycle stages are described. The results of the solution of the model problem that are obtained using each network is performed are comparatively analyzed. |
| Databáze: | OpenAIRE |
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