• DocumentCode
    395159
  • Title

    Implementation of H∞-learning and its analysis

  • Author

    Nishiyama, Kiyoshi

  • Author_Institution
    Dept. of Comp. & Inf. Sci., Iwate Univ., Japan
  • Volume
    1
  • fYear
    2002
  • fDate
    18-22 Nov. 2002
  • Firstpage
    377
  • Abstract
    This paper studies implementation of the H∞-learning and unified approach to analyze the backpropagation and H2-learning as well as the H∞-learning. Various forms of H∞-learning algorithms are developed from the tradeoff between the learning performance and computational complexity. Also, an unified update formula of weight vector is derived.
  • Keywords
    backpropagation; computational complexity; feedforward neural nets; state-space methods; backpropagation; computational complexity; learning algorithm; multilayered feedforward network; multilayered neural networks; state-space model; supervised learning; Algorithm design and analysis; Artificial neural networks; Backpropagation algorithms; Computational complexity; Multi-layer neural network; Neural networks; Neurons; Robustness; Supervised learning; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
  • Print_ISBN
    981-04-7524-1
  • Type

    conf

  • DOI
    10.1109/ICONIP.2002.1202197
  • Filename
    1202197
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=395159