Application of the volume learning algorithm artificial neural networks for recognition of the type of interaction between neurons from their cross-correlation histograms
An algorithm based on two types artificial neural networks (ANNs) is proposed. The first network is an associative ANN while the second network is a Self-Organizing Map of Kohonen. The results for a test set are similar to the performance of our pre-vious expert system algorithm developed with Group...
Збережено в:
| Дата: | 2005 |
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| Автори: | , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Навчально-науковий комплекс "Інститут прикладного системного аналізу" НТУУ "КПІ" МОН та НАН України
2005
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| Теми: | |
| Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/14089 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Application of the volume learning algorithm artificial neural networks for recognition of the type of interaction between neurons from their cross-correlation histograms / V.V. Kovalishyn, I.V. Tetko // Систем. дослідж. та інформ. технології. — 2005. — № 3. — С. 48-56. — Бібліогр.: 20 назв. — англ. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| Резюме: | An algorithm based on two types artificial neural networks (ANNs) is proposed. The first network is an associative ANN while the second network is a Self-Organizing Map of Kohonen. The results for a test set are similar to the performance of our pre-vious expert system algorithm developed with Group Method of Data Handling (GMDH). However, while GMDH uses indices derived using the expert knowledge (and thus require considerable time and resources) the VLA process initial raw data. |
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