• DocumentCode
    315248
  • Title

    A structural learning algorithm for multi-layered neural networks

  • Author

    Kotani, Manabu ; Kajiki, Akihiro ; Akazawa, Kenzo

  • Author_Institution
    Fac. of Eng., Kobe Univ., Japan
  • Volume
    2
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    1105
  • Abstract
    We propose a new structural learning algorithm for organizing the structure of the multi-layered neural networks. The proposed pruning algorithm consists of two already known algorithms, the structural learning algorithm with forgetting and the optimal brain damage algorithm using the second derivatives of the assessment. After the network is slimmed by the structural learning algorithm with forgetting, unimportant weights are pruned from the network using the second derivatives. The simulations are performed for the Boolean function and the acoustic diagnosis of compressors. The results show that the proposed algorithm is effective for eliminating the unimportant weights
  • Keywords
    acoustic signal processing; compressors; fault diagnosis; learning (artificial intelligence); multilayer perceptrons; Boolean function; acoustic diagnosis; forgetting; multi-layered neural networks; optimal brain damage algorithm; pruning algorithm; structural learning algorithm; unimportant weights; Acoustic propagation; Biological neural networks; Boolean functions; Brain modeling; Convergence; Iterative algorithms; Multi-layer neural network; Neural networks; Pattern recognition; Speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.616184
  • Filename
    616184