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Type-2 fuzzy logic systems in applications: managing data in selective catalytic reduction for air pollution prevention

Niewiadomski Adam, Kacprowicz Marcin

Journal of Artificial Intelligence and Soft Computing Research

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2021

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Vol. 11, No. 2

85--97

EN

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.

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On two functional equations connected with distributivity of fuzzy implications

Ger R., Kuczma M. E., Niemyska W.

Commentationes Mathematicae

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2015

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Vol 55, No. 2

163--169

EN

The distributivity law for a fuzzy implication I:[0,1]2→[0,1] with respect to a fuzzy disjunction S:[0,1]2→[0,1] states that the functional equation I(x,S(y,z))=S(I(x,y),I(x,z)) is satisfied for all pairs (x,y) from the unit square. To compare some results obtained while solving this equation in various classes of fuzzy implications, Wanda Niemyska has reduced the problem to the study of the following two functional equations: h(min(xg(y),1))=min(h(x)+h(xy),1), x∈(0,1), y∈(0,1], and h(xg(y))=h(x)+h(xy), x,y∈(0,∞), in the class of increasing bijections h:[0,1]→[0,1] with an increasing function g:(0,1]→[1,∞) and in the class of monotonic bijections h:(0,∞)→(0,∞) with a function g:(0,∞)→(0,∞), respectively. A description of solutions in more general classes of functions (including nonmeasurable ones) is presented.

3

Improving the quality of the fetal state assessment with epsilon-insensitive learning methods

Czabański R., Wróbel J., Jeżewski J., Łęski J.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

19--26

EN

Recording and analysis of fetal heart rate (FHR) signal is nowadays the primary method for the biophysical assessment of the fetal state. Since the correct interpretation of crucial FHR characteristics is difficult, methods of automated quantitative signal evaluation are still the subject of the research studies. In the following paper we investigated the possibility of improvement of the fetal state evaluation on the basis of the epsilon-insensitive learning (eIL). We examined two eIL procedures integrated with fuzzy clustering algorithms as well as different methods of logical interpretation of the fuzzy conditional statements. The quality of the FHR signal classification was evaluated using the data collected with the computerized fetal surveillance system. The learning performance was measured with the number of correct classification (CC) and overall quality index (QI) defined as a geometric mean of sensitivity and specificity. The obtained results (CC = 88 % and QI = 87 %) show a high efficiency of the fetal state assessment using the epsilon-insensitive learning based methods.

4

Weak fuzzy implication algebras and induced structures

Chajda I., Emanovsky P., Halas R.

Demonstratio Mathematica

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2006

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Vol. 39, nr 1

1-14

EN

The concept of fuzzy implication algebra is weakened replacing the exchange axiom by an essentially weaker version. To each such algebra a groupoid is assigned. We get conditions under which this groupoid is commutative and show when a fuzzy implication algebra becomes a lattice with antitone involutions on all sections.