Name
Abstract | ||
|---|---|---|
Ranking fuzzy numbers is very necessary when we have to make a decision with imprecise information. The comparison depends on decision-maker's subjectivity and then capturing it into algorithms is difficult. Several methods has been developed in order to ranking fuzzy numbers, each of them being subjective, but the lack of real fitness is always present. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1007/BFb0035928 | IWANN |
Keywords | Field | DocType |
ranking fuzzy number. i- introduction.,aid decision,we propose ranking fuzzy numbers using anns. we present several experien- ces: we have simulated an ann that use the backpropagation algorithm for learning. also we show that it is possible to take a decision using anns,artificial neural networks,trapezoidal fuzzy number,when we have fuzzy information. keywords: anns,in this paper,backpropagation,backpropagation algorithm,decision making process,artificial neural network,fuzzy number | Neuro-fuzzy,Computer science,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Backpropagation,Artificial neural network,Rprop,Machine learning,Decision-making | Conference |
ISBN | Citations | PageRank |
3-540-54537-9 | 2 | 1.69 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Cano | 1 | 50 | 8.32 |
| Miguel Delgado | 2 | 106 | 7.81 |
| Ignacio Requena | 3 | 41 | 9.03 |