• DocumentCode
    1406485
  • Title

    Modeling stripline discontinuities by neural network with knowledge-based neurons

  • Author

    Wang, Bing-Zhong ; Zhao, Deshuang ; Hong, Jingsong

  • Author_Institution
    Inst. of Appl. Phys., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
  • Volume
    23
  • Issue
    4
  • fYear
    2000
  • fDate
    11/1/2000 12:00:00 AM
  • Firstpage
    692
  • Lastpage
    698
  • Abstract
    A three-layer neural network with knowledge-based neurons in the hidden layer (NNKBN) is presented for modeling stripline discontinuities. In NNKBN, prior knowledge for stripline discontinuity is incorporated into each hidden neuron. With knowledge-based neurons, the learning ability and generalization of the neural network are improved. Compared with conventional multi-layer perceptron neural network, the NNKBN can map the input-output relationships with fewer hidden neurons and has higher reliability for extrapolation beyond training data range. Two examples are given to illustrate the potential power of this approach.
  • Keywords
    circuit CAD; digital integrated circuits; extrapolation; high-speed integrated circuits; integrated circuit design; integrated circuit modelling; learning (artificial intelligence); multilayer perceptrons; strip line discontinuities; HSDICs; extrapolation; hidden layer; high-speed digital ICs; input-output relationships; knowledge-based neurons; learning ability; stripline discontinuities; three-layer neural network; training data range; Artificial neural networks; Extrapolation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Stripline; Training data; Transmission line discontinuities; Vectors;
  • fLanguage
    English
  • Journal_Title
    Advanced Packaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1521-3323
  • Type

    jour

  • DOI
    10.1109/6040.883760
  • Filename
    883760