Pub Date : 1900-01-01DOI: 10.7494/978-83-66727-48-9_7
A. Macioł, P. Macioł
The paper describes a solution of a problem of developing of fuzzy rules compliant with the Takagi–Sugeno approach where a number of available examples (observations) is not sufficient. Modeling of fuzzy premises and generating functions describing a dependence of a result variable on antecedents are described. In our original approach a problem of identification of membership functions of variables com-posing premises and a problem of consequent parameters identification are solved. For the first one, we used a simple technique based on individual judgements of experts. The second one is solved with a linear programming method. In particular, our approach to formulate the consequent parameter identification problem allows using of an extremely effective T-S method when a data-driven approach cannot be applied. In the paper we present a description of our methods and results of simulations of accuracy of the proposed approach, based on commonly known benchmarks. The achieved accuracy of classification is sufficient for the most of decision-making systems of an expert nature.
{"title":"Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient","authors":"A. Macioł, P. Macioł","doi":"10.7494/978-83-66727-48-9_7","DOIUrl":"https://doi.org/10.7494/978-83-66727-48-9_7","url":null,"abstract":"The paper describes a solution of a problem of developing of fuzzy rules compliant with the Takagi–Sugeno approach where a number of available examples (observations) is not sufficient. Modeling of fuzzy premises and generating functions describing a dependence of a result variable on antecedents are described. In our original approach a problem of identification of membership functions of variables com-posing premises and a problem of consequent parameters identification are solved. For the first one, we used a simple technique based on individual judgements of experts. The second one is solved with a linear programming method. In particular, our approach to formulate the consequent parameter identification problem allows using of an extremely effective T-S method when a data-driven approach cannot be applied. In the paper we present a description of our methods and results of simulations of accuracy of the proposed approach, based on commonly known benchmarks. The achieved accuracy of classification is sufficient for the most of decision-making systems of an expert nature.","PeriodicalId":165954,"journal":{"name":"Nauka – Technika – Technologia. Tom 2","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115551830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7494/978-83-66727-48-9_10
Siddhesh Raorane, P. Paćko
The spatial grid of numerical models of acoustic emission (AE) sources acts as a spatial filter for elastic wave signals. The filtering effects are particularly prominent for short-time, broadband signals – typical for AE. In this paper, we investigate the filtering influence of spatial discretization (meshing) on broadband AE source modeling. The AE source – generating AEs propagating as elastic waves – was modeled using cohesive zone approach, and the numerical simulations were performed in commercial FEM software COMSOL Multiphysics. Results were processed using Fast Fourier Transform, filtered, and subsequently analyzed in terms of the filtering effects of spatial discretization on AE source modeling. In this paper, it is shown that spatial grids in numerical models effectively work like low-pass filters with the cut-off frequency corresponding to the numerical Brillouin zone. The latter induces short wavelength limitation, and the frequency components near (below) the zone edge are amplified in magnitude. It was found that the amplified frequencies represent numerical errors. Also, it was inferred that the filtering effect of spatial discretization can mask the AE source characteristics and affect the quality of the results.
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Pub Date : 1900-01-01DOI: 10.7494/978-83-66727-48-9_9
M. Niekurzak
{"title":"Analiza możliwości rozwoju i wykorzystania potencjału biogazu rolniczego na cele energetyczne w Polsce","authors":"M. Niekurzak","doi":"10.7494/978-83-66727-48-9_9","DOIUrl":"https://doi.org/10.7494/978-83-66727-48-9_9","url":null,"abstract":"","PeriodicalId":165954,"journal":{"name":"Nauka – Technika – Technologia. Tom 2","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127684217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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