Name
Title | ||
|---|---|---|
Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems |
Abstract | ||
|---|---|---|
Abstract: Antipollution legislation in automotive internal combustion engines requires active control and prediction of pollutant formation and emissions. Predictive emission models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks. In this paper, fuzzy modelling of the NOx emissions of a diesel engine is investigated, which overcomes some drawbacks of pure engine mapping or analytical physical-oriented models. For building up the fuzzy NOx prediction models, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically extracts an appropriate number of rules and fuzzy sets by an evolving version of vector quantization (eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional. The predictive power of the fuzzy NOx prediction models is compared with that one achieved by physical-oriented models based on high-dimensional engine data recorded during steady-state and dynamic engine states. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.asoc.2010.10.004 | Appl. Soft Comput. |
Keywords | Field | DocType |
pure engine mapping,fuzzy set,high-dimensional data,steady-state and dynamic engine states,takagi-sugeno fuzzy system,high-dimensional engine data,analytical physical-oriented models,fuzzy modelling,dynamic prediction model,nox emission,flexfis,diesel engine,nox emissions,takagi-sugeno fuzzy systems,dynamic engine state,engine control,fuzzy nox prediction model,combustion engines,automotive internal combustion engine,fuzzy system,high dimensional data,prediction model,steady state | Least squares,Mathematical optimization,Control theory,Fuzzy logic,Fuzzy set,Control engineering,Vector quantization,Fuzzy control system,Predictive modelling,Diesel engine,Mathematics,Automotive industry | Journal |
Volume | Issue | ISSN |
11 | 2 | Applied Soft Computing Journal |
Citations | PageRank | References |
25 | 1.12 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edwin Lughofer | 1 | 1940 | 99.72 |
| Vicente Macián | 2 | 28 | 1.55 |
| Carlos Guardiola | 3 | 42 | 2.61 |
| Erich Peter Klement | 4 | 989 | 128.89 |