Abstract
In the textile industry, cloth is made from many different brands of yarns, while yarns are made from various different kinds of cotton. Naturally, we want to achieve a full-fledged utilization of combining different brands of cotton to produce yarns with the best quality. Such a combination of different brands of cotton in the manufacturing process is called cotton allocation. In this regard, it is often encountered a problem of how to select a substitute (from the warehouse) for a brand of cotton in the cotton allocation that is currently in use but running out of the raw material. A model that uses Fuzzy Dynamic Integrated Judgment (FDIJ) provides a very useful tool in resolving this problem. In view of several factors in cotton allocations, the FDIJ is made for cotton selection according to the data feedback from previous production runs. In [4], we explained how the data feedback can be stored in a fuzzy dynamic relationship dictionary which generates a fuzzy dynamic relation to be used in the learning ability in the problem of cotton allocation. It should be noted that finding a fuzzy weight set in the FDIJ is an inverse operation of fuzzy integrated judgment. In applications, it requires an initial fuzzy weight set (or the so-call the original image) to make the whole process work. The main objective of this paper is to explore a practical approach of obtaining the optimal fuzzy weight set for building a model of FDIJ whether or not the exact original image exists.
| Original language | English |
|---|---|
| Title of host publication | Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS |
| Pages | 137-141 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 2006 |
| Event | NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society - Montreal, QC, Canada Duration: Jun 3 2006 → Jun 6 2006 |
Other
| Other | NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society |
|---|---|
| Country | Canada |
| City | Montreal, QC |
| Period | 6/3/06 → 6/6/06 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Media Technology
Cite this
The optimal fuzzy weight set for building a model of fuzzy integrated judgment. / Cheng, Shih-Chuan.
Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS. 2006. p. 137-141 4216790.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - The optimal fuzzy weight set for building a model of fuzzy integrated judgment
AU - Cheng, Shih-Chuan
PY - 2006
Y1 - 2006
N2 - In the textile industry, cloth is made from many different brands of yarns, while yarns are made from various different kinds of cotton. Naturally, we want to achieve a full-fledged utilization of combining different brands of cotton to produce yarns with the best quality. Such a combination of different brands of cotton in the manufacturing process is called cotton allocation. In this regard, it is often encountered a problem of how to select a substitute (from the warehouse) for a brand of cotton in the cotton allocation that is currently in use but running out of the raw material. A model that uses Fuzzy Dynamic Integrated Judgment (FDIJ) provides a very useful tool in resolving this problem. In view of several factors in cotton allocations, the FDIJ is made for cotton selection according to the data feedback from previous production runs. In [4], we explained how the data feedback can be stored in a fuzzy dynamic relationship dictionary which generates a fuzzy dynamic relation to be used in the learning ability in the problem of cotton allocation. It should be noted that finding a fuzzy weight set in the FDIJ is an inverse operation of fuzzy integrated judgment. In applications, it requires an initial fuzzy weight set (or the so-call the original image) to make the whole process work. The main objective of this paper is to explore a practical approach of obtaining the optimal fuzzy weight set for building a model of FDIJ whether or not the exact original image exists.
AB - In the textile industry, cloth is made from many different brands of yarns, while yarns are made from various different kinds of cotton. Naturally, we want to achieve a full-fledged utilization of combining different brands of cotton to produce yarns with the best quality. Such a combination of different brands of cotton in the manufacturing process is called cotton allocation. In this regard, it is often encountered a problem of how to select a substitute (from the warehouse) for a brand of cotton in the cotton allocation that is currently in use but running out of the raw material. A model that uses Fuzzy Dynamic Integrated Judgment (FDIJ) provides a very useful tool in resolving this problem. In view of several factors in cotton allocations, the FDIJ is made for cotton selection according to the data feedback from previous production runs. In [4], we explained how the data feedback can be stored in a fuzzy dynamic relationship dictionary which generates a fuzzy dynamic relation to be used in the learning ability in the problem of cotton allocation. It should be noted that finding a fuzzy weight set in the FDIJ is an inverse operation of fuzzy integrated judgment. In applications, it requires an initial fuzzy weight set (or the so-call the original image) to make the whole process work. The main objective of this paper is to explore a practical approach of obtaining the optimal fuzzy weight set for building a model of FDIJ whether or not the exact original image exists.
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UR - http://www.scopus.com/inward/citedby.url?scp=46749145345&partnerID=8YFLogxK
U2 - 10.1109/NAFIPS.2006.365874
DO - 10.1109/NAFIPS.2006.365874
M3 - Conference contribution
AN - SCOPUS:46749145345
SN - 1424403634
SN - 9781424403639
SP - 137
EP - 141
BT - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
ER -