Possibility Data Analysis
| Project/Area Number |
06680404
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
社会システム工学
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| Research Institution | University of Osaka prefecture |
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Principal Investigator |
TANAKA Hideo University of Osaka Prefecture Professor, 工学部, 教授 (20081408)
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| Co-Investigator(Kenkyū-buntansha) |
ISHIBUCHI Hisao University of Osaka Prefecture Associate Professor, 工学部, 助教授 (60193356)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| 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)
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| Keywords | Possibility Distribution / Possibility Data Analysis / Possibility Portfolio / Identification / Fuzzy Neural Networks / Incomplete Information / Interval Data / Classification Problem / 可能性分布の同定 |
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| 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.
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Report
(3 results)
Research Products
(9 results)
