Fuzzy Neural Networks with Fuzzy Weights

• ISHIBUCHI Hisao

  University of Osaka Prefecture

• OKADA Hidehiko

  Kansai C & C Research Laboratory, NEC Corpolation

• TANAKA Hideo

  University of Osaka Prefecture

Bibliographic Information

Other Title

• ファジィ数結合強度を持つファジィニューラルネット
• ファジィスウ ケツゴウ キョウド オ モツ ファジィ ニューラル ネット

Abstract

In this paper, we first propose an architecture of neural networks that have fuzzy weights and fuzzy biases. A neural network with the proposed architecture maps an input vector of real numbers to an output of fuzzy number. Therefore the neural network can be viewed as an approximator of non-linear fuzzy functions from real vectors to fuzzy numbers. Next we define a cost function using the fuzzy output from the neural network and the corresponding fuzzy target output. A learning algorithm can be derived from the cost function in a similar manner as the BP (Back-Propagation) algorithm. We also define two variations of the cost function that are based on the inclusion relation between the fuzzy output and the fuzzy target. Two learning algorithms can be also derived from the two cost functions. The derived learning algorithms are illustrated by computer simulations for numerical examples.

Journal

• Transactions of the Institute of Systems, Control and Information Engineers

  Transactions of the Institute of Systems, Control and Information Engineers 6 (3), 137-148, 1993

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Keywords

• fuzzy neural network
• BP algorithm
• fuzzy data
• fuzzy arithmetic
• fuzzy regression analysis

Details

• CRID: 1390001205165618688
• NII Article ID: 10007137024
• NII Book ID: AN1013280X
• DOI: 10.5687/iscie.6.137
• ISSN: 2185811X
• ISSN: 13425668
• NDL BIB ID: 3803401
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I3803401
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed