<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO<sub>2</sub>). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.</p></abstract>

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective...

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Online veröffentlicht: 29. Jan. 2021

Seitenbereich: 85 - 97

Eingereicht: 26. März 2020

Akzeptiert: 05. Nov. 2020

DOI: https://doi.org/10.2478/jaiscr-2021-0006

Schlüsselwörter
Selective Catalytic Reduction (SCR), fuzzy management of DeNOx filter, fuzzy logic systems, ”engineering” fuzzy implications, learning fuzzy rules

© 2021 Adam Niewiadomski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Online veröffentlicht: 29. Jan. 2021

Seitenbereich: 85 - 97

Eingereicht: 26. März 2020

Akzeptiert: 05. Nov. 2020

DOI: https://doi.org/10.2478/jaiscr-2021-0006

SchlüsselwörterSelective Catalytic Reduction (SCR), fuzzy management of DeNOx filter, fuzzy logic systems, ”engineering” fuzzy implications, learning fuzzy rules

© 2021 Adam Niewiadomski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Selective Catalytic Reduction (SCR), fuzzy management of DeNOx filter, fuzzy logic systems, ”engineering” fuzzy implications, learning fuzzy rules