Robust adaptive learning approach to self-organizing maps
| Autor: | Bekir Karlik, Mohammad Shukri Salman, Gülden Eleyan, Alaa Ali Hameed |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Self-organizing map
Information Systems and Management Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Contrast (statistics) Pattern recognition 02 engineering and technology Management Information Systems Range (mathematics) Variable (computer science) ComputingMethodologies_PATTERNRECOGNITION Rate of convergence Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Applications of artificial intelligence Artificial intelligence Adaptive learning business Software |
| Zdroj: | Knowledge-Based Systems. 171:25-36 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2019.01.011 |
| Popis: | The Self-Organizing Map (SOM) has attracted much research in diverse fields of study, where it has been successfully applied to a wide range of artificial intelligence applications. In this paper, a new intelligent adaptive learning SOM, in contrast with the conventional SOM algorithm, is presented. The proposed SOM overcomes the disadvantages of the conventional SOM by deriving a new variable learning rate that can adaptively achieve the optimal weights and obtain the winner neurons with a shorter learning process time. To quantify the performance, the proposed SOM was compared with various unsupervised algorithms to examine how well the network weight update equation adapts and reaches the optimum weights with fewer iterations. Extensive experiments were conducted using eight different databases from the UCI and KEEL repositories. The proposed SOM algorithm was further compared with the conventional SOM, GF-SOM, PLSOM, and PLSOM2 algorithms. The proposed SOM algorithm showed superiority in terms of the convergence rate, Quantization Error (QE), Topology Error (TE) preserving map, and accuracy during the classification process. |
| Databáze: | OpenAIRE |
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