Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making
Dilip Kumar Yadav, Harikesh Bahadur Yadav
Dilip Kumar Yadav
Available Online June 2015.
- https://doi.org/10.2991/ifsa-eusflat-15.2015.79How to use a DOI?
- Membership function, decision tree, fuzzy rule, histogram analysis, entropy.
- Nowadays, decision making using fuzzy logic is a ma-jor research area for scientists, researchers and project managers. Construction of membership functions and fuzzy rules from numerical data is very important in various applications of the fuzzy set theory. Therefore, in this paper a model is proposed for development of membership functions and fuzzy rules from numerical data for decision making. The main advantage of the proposed model is its simplicity. The proposed model is applied on Fisher’s Iris data for decision making. The validation result shows that proposed model has a higher accuracy than existing models.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Dilip Kumar Yadav AU - Harikesh Bahadur Yadav PY - 2015/06 DA - 2015/06 TI - Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 551 EP - 555 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.79 DO - https://doi.org/10.2991/ifsa-eusflat-15.2015.79 ID - Yadav2015/06 ER -