Name
Abstract | ||
|---|---|---|
The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a denazification mechanism that is `differentiable' with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FUZZ-IEEE.2014.6891815 | FUZZ-IEEE |
Keywords | Field | DocType |
fuzzy set theory,knowledge based systems,frbs,mamdani fuzzy rule based system,denazification mechanism,adaptive mamdani-type fuzzy modeling strategy,back error propagation,back error propagation algorithm,industrial gas turbines,bep algorithm,adaptive mamdani fmzy modeling,industrial gas turbine,amfm,power engineering computing,membership functions,mamdani fmzy rule based system,gas turbines,training data,predictive models,computational modeling,prediction algorithms,engines | Propagation of uncertainty,Industrial gas,Computer science,Industrial systems,Control theory,Fuzzy logic,Gas compressor,Turbine,Artificial intelligence,System identification,Membership function,Machine learning | Conference |
ISSN | Citations | PageRank |
1544-5615 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhang | 1 | 294 | 98.00 |
| Jun Chen | 2 | 1 | 3.41 |
| C. M. Bingham | 3 | 143 | 23.30 |
| Mahdi Mahfouf | 4 | 235 | 33.17 |