Name
Abstract | ||
|---|---|---|
This paper discusses a knowledge-base encoding methodology for diagnostic tasks. It transform "expert"-provided rules into algebraic expressions so inference of the "Possible" disorders is carried out via associated constrained optimisation problems. In this way, the need of conventional fuzzy inference systems or "uncertain"-logic schemes is no longer present in the particular setting in this paper. An oil-analysis diagnosis case study is presented as an application example, with actual experimental data. The problem is solved by efficient linear programming tools, in principle able to cope with large-scale problems. The only software used was Mathematica (R) 5.2. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FUZZY.2007.4295582 | 2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4 |
Keywords | Field | DocType |
logic,knowledge base,linear programming,oil analysis,fuzzy sets,engines,constraint optimization,linear program,petroleum industry,encoding,petroleum,fuzzy systems,knowledge based systems | Experimental data,Inference,Computer science,Fuzzy logic,Knowledge-based systems,Software,Linear programming,Artificial intelligence,Algebraic expression,Machine learning,Encoding (memory) | Conference |
ISSN | Citations | PageRank |
1098-7584 | 4 | 0.53 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Sala | 1 | 562 | 33.44 |
| Julio C. Ramirez | 2 | 4 | 0.53 |
| B. Tormos | 3 | 10 | 3.16 |
| Manuel Yago | 4 | 4 | 0.53 |