New training strategies for RBF neural networks for X-ray agricultural product inspection
| Autor: | David P. Casasent, Xue-wen Chen |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Artificial neural network
Computer science business.industry Feature extraction Pattern recognition Product inspection Machine learning computer.software_genre Artificial Intelligence Signal Processing Radial basis function Computer Vision and Pattern Recognition Artificial intelligence Cluster analysis business computer Software |
| Zdroj: | Pattern Recognition. 36:535-547 |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/s0031-3203(02)00058-4 |
| Popis: | Classification of real-time X-ray images of pistachio nuts is discussed. The goal is to reduce the percentage of infested nuts while not rejecting more than a few percent of the good nuts. Radial basis function (RBF) neural network classifiers are emphasized. New training procedures are developed that allow samples such as those that are near decision boundaries to be treated differently from other samples. New clustering methods and new cluster classes are advanced to select and separately control various RBF parameters. These advancements are shown to be of use in this application. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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