關鍵字查詢 | 類別:期刊論文 | | 關鍵字:Evolutional RBFNs image model describing-based segmentation system designs

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序號 學年期 教師動態
1 106/1 電機系 翁慶昌 教授 期刊論文 發佈 Evolutional RBFNs image model describing-based segmentation system designs , [106-1] :Evolutional RBFNs image model describing-based segmentation system designs期刊論文Evolutional RBFNs image model describing-based segmentation system designsH.M. Feng; C.C. Wong; J.H. Horng; L.Y. LaiRBFNs;Bacterial foraging particle swarm optimization;Recursive least-squares;Image segmentationNeurocomputing 272, p.374-385Knowledge discovered-based radial basis function neural networks (RBFNs) model can describe an appropriate behaviors of identified image patterns through the multiple and hybrid learning schemes. The image data extraction learning algorithm (IDELA) with dynamic recognitions to automatically match the appropriate feature with a suitable number of radial basis function (RBFs). This first step approaches their associated centers positions to extract initial prototypes. The approximated image model as a describer is automatically generated by the RBFPSO learning scheme, which is contained hybrid bacterial foraging particle swarm optimization (BFPSO) algorithm and recursive leas
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