About the group approach in the task of fuzzy synthetic evaluation
Keywords:
synthetic method, group approach, weight criteria, fuzzy classificationAbstract
The fuzzy synthetic evaluation method can be applied to problems where we need to evaluate object determined by various heterogeneous features. The problem is to determine quantitatively significances of various features th6666/at is their weights. Using various weight vectors leads to the different results of evaluation. There are various methods to define weight vectors but there is no criterion to determine the best of them. The work is devoted to the problem of determining the balance in the method of fuzzy synthetic evaluation sites. The paper proposes the use of the cluster approach to determine the weights of the criteria which in a sense, a universal and can be applied to various modifications of this method. We establish a connection between the fuzzy synthetic evaluation method and fuzzy classifications and propose a group approach to determine weights of the method. Also, the article describes the proof of the theorem, which determines the weight of the criteria for the group approach.
References
[2] Bohui Pang, Shizhen Bai. An integrated fuzzy synthetic evaluation approach for supplier selection based on analytic network process // Journal of Intelligent Manufacturing. Volume 24, Issue 1, 2013, - pp.163-174.
[3] Chang, Ni-Bin, Chen, H. W., Ning, S. K. Identification of river water quality using the Fuzzy Synthetic Evaluation approach //Journal of Environmental Management. -(2001). - 63(3), - pp.293-305.
[4] Fachao Li, Wenfang Wang, Yan Shi2 and Chenxia Jin. Fuzzy synthetic evaluation model based on the knowledge system // International Journal of Innovative Computing: Information and Contro. Volume 9, - Number 10, - October, 2013, - pp.4073-4084.
[5] Gao, Z., Zhong, Q., An, M. Fuzzy Integration Method of Synthetic Evaluation for Traffic and Transportation Systems //Proceedings of the Second International Conference on Transportation and Traffic. Studies -2000, - pp.211-223.
[6] Gorai A. K., Kanchan , Upadhyay A., Goyal P. Design of fuzzy synthetic evaluation model for air quality assessment. // Environment Systems and Decisions. Volume 34, Issue 3, - September, 2014 - pp 456-469.
[7] Hu, B. Q., Lo, S. M., Liu, M., Zhao, C. M. On the Use of Fuzzy Synthetic Evaluation and Optimal Classification for Fire Risk Ranking of Buildings //Neural Computing and Application -2009, - 2, - pp.113-127.
[8] Khan F , Sadiq R. Risk-based prioritization of air pollution monitoring using fuzzy synthetic evaluation technique //Environ Monit Assess. - Jun, 2005, - 105(1-3), - pp.261-83
[9] Sudhir Dahiya, Bupinder Singh, Shalini Gaur, V.K. Garg, H.S. Kushwaha Analysis of groundwater quality using fuzzy synthetic evaluation //Journal of Hazardous Materials. Volume 147, Issue 3, - 2007, - pp.938–946
[10] Tesfamaraim, S., Saatcioglu, M. Seismic Risk Assessment of RC Buildings Using Fuzzy Synthetic Evaluation //Journal of Earthquake Engineering. -2008, - 12(7), -pp.1157-1184.
[11] Zadeh, L. A. Fuzzy sets and their application to pattern classification and clustering analysis. //Fuzzy sets, fuzzy logic, and fuzzy systems. -1996, - pp.355-393.
