English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 52512/87668 (60%)
造訪人次 : 9351001      線上人數 : 274
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115537

    題名: Evolutional RBFNs image model describing-based segmentation system designs
    作者: H.M. Feng;C.C. Wong;J.H. Horng;L.Y. Lai
    關鍵詞: RBFNs;Bacterial foraging particle swarm optimization;Recursive least-squares;Image segmentation
    日期: 2018-01-10
    上傳時間: 2018-11-08 12:11:05 (UTC+8)
    摘要: Knowledge 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 least-squares (RLS) iterations to deeply approach the image feature. Due to the limitations and possible local learning trap, K-means, differential evolution (DE) and particle swarm optimization (PSO) learning algorithms cannot obtain the most smaller Root-Mean-Square Error (RMSE) to achieve an appropriate image segmentation in all experiment cases. The constructed RBFNs image model is generated by the support of multiple image self-extraction feature machine (MISEFM), which combined IDELA and RBFPSO algorithms to develop the universal RBFNs image describers. Simulations compared with other K-means, PSO and DE learning methods, show the average great performance in several real image segmentation applications. The peak signal-to-noise ratio (PSNR) index is selected to evaluate the quality of the reconstructed images. Simulations show that the evolutional hybrid and multi-level RBFNs image model-based system is determined to simultaneously achieve both high performance indexes on accuracy (RMSE) and a high image quality description (PSNR) for matching the desired characters and behaviors of image patterns within a fewer RBFs functions.
    關聯: Neurocomputing 272, p.374-385
    DOI: 10.1016/j.neucom.2017.07.006
    顯示於類別:[電機工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋