• DocumentCode
    1748813
  • Title

    Integrating RBF networks with domain knowledge

  • Author

    McGarry, K. ; MacIntyre, J. ; Addison, D.

  • Author_Institution
    Sch. of Comput. Eng. & Technol., Univ. of Sunderland, UK
  • Volume
    3
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1902
  • Abstract
    Often in real-world situations no actual data is available but a domain expert may have a good idea of what to expect in terms of input and output parameter values. If the expert can express these relationships in the form of rules, this would provide a resource too valuable to ignore. The authors illustrate how a single domain rule can be used to synthesize new RBF hidden unit parameters. These new hidden units are incorporated into the original neural network to enable classification on examples the network would otherwise been unable to recognise. The authors then describe a more complex application using fuzzy logic to insert domain knowledge into the RBF network. Fuzzy logic is particularly suited to manage the imprecision and vagueness of natural language. The fuzzy rules are used to synthesize new RBF hidden unit parameters for incorporation into a new or existing network
  • Keywords
    condition monitoring; fuzzy logic; fuzzy set theory; learning (artificial intelligence); radial basis function networks; RBF networks; domain expert; domain knowledge; fuzzy logic; hidden unit parameters; imprecision; natural language; single domain rule; vagueness; Data engineering; Fault diagnosis; Fuzzy logic; Fuzzy sets; Fuzzy systems; Natural languages; Network synthesis; Neural networks; Radial basis function networks; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.938454
  • Filename
    938454