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Possibility Data Analysis

Research Project

Project/Area Number 06680404
Research Category

Grant-in-Aid for General Scientific Research (C)

Allocation TypeSingle-year Grants
Research Field 社会システム工学
Research InstitutionUniversity of Osaka prefecture

Principal Investigator

TANAKA Hideo  University of Osaka Prefecture Professor, 工学部, 教授 (20081408)

Co-Investigator(Kenkyū-buntansha) ISHIBUCHI Hisao  University of Osaka Prefecture Associate Professor, 工学部, 助教授 (60193356)
Project Period (FY) 1994 – 1995
Project Status Completed (Fiscal Year 1995)
Budget Amount *help
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1995: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1994: ¥1,300,000 (Direct Cost: ¥1,300,000)
KeywordsPossibility Distribution / Possibility Data Analysis / Possibility Portfolio / Identification / Fuzzy Neural Networks / Incomplete Information / Interval Data / Classification Problem / 可能性分布の同定
Research Abstract

The following results were obtained by this research whose aims were to propose new data analysis methods based on the concept of possibility destribution, to implement the proposed methods as computer programs, and to examine the ability of each method by applying it to real-world problems.
1. An identification method was proposed to determine a possibility distribution of the coefficients of a possibility regression model from numerical data. The proposed method was implemented as a computer program, and its performance was examined by the application to prefabricated house price data.
2. An identification method was proposed to determine a possibility distribution of each class in a multi-dimensional pattern space. The identified possibility distribution was linearly mapped into a lower dimensional space by a characteristic vector. A linear propramming problem was formulated to determine this characteristic vector in order to separate the possibility distribution of one class from those of the other classes.
3. A non-linear possibility regression method was proposed using fuzzy neural networks. A learning algorithm was derived to adjust triangular shape fuzzy connection weights.
4. A fuzzy-rule-based regression method was proposed where the membership function of each antecedent fuzzy set was viewed as a possibility distribution. The proposed method was compared with a neural-network-based method by applying them to rice taste data.

Report

(3 results)
  • 1995 Annual Research Report   Final Research Report Summary
  • 1994 Annual Research Report
  • Research Products

    (9 results)

All Other

All Publications (9 results)

  • [Publications] 田中英夫: "指数型可能性判別分析" 日本ファジィ学会誌. 6. 1147-1160 (1994)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] Hisao Ishibuchi: "A Learning Algorithm of Fuzzy Neural Networks with Triangular Fuzzy Weights" International Journal of Fuzzy Sets and Systems. 71. 277-293 (1995)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] Ken Nozaki: "A Simple but Powerful Heuristic Method for Generating Fuzzy Rules from Numerical Data" International Journal of Fuzzy Sets and Systems. (発表予定).

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] 田中英夫: "ソフトデータ解析" 朝倉書店, 173ページ (1995)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] Hideo Tanaka: "Exponential Possibility Discriminant Analysis" Journal of Japan Society for Fuzzy Theory and Systems (in Japanese). Vol.6, No.6. 1147-1160 (1994)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] Hisao Ishibuchi: "A Learning Algorithm of Fuzzy Neural Networks with Triangular Fuzzy Weights" International Journal of Fuzzy Sets and Systems. Vol.71, No.3. 277-293 (1995)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] Ken Nozaki: "A Simple but Powerful Heuristic Method for Generating Fuzzy Rules from Numerical Data" International Journal of Fuzzy Sets and Systems.

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] Ken Nozaki: Asakura Publishing Company. Soft Data Analysis (in Japanese), 173 (1995)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1995 Final Research Report Summary
  • [Publications] 田中英夫: "指数型可能性判別分析" 日本ファジィ学会誌. 6. 1147-1160 (1994)

    • Related Report
      1994 Annual Research Report

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Published: 1994-04-01   Modified: 2016-04-21  

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