Probabilistic-Fuzzy Inference with Piecewise Linear Quantile Regression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F25%3AA2603BTE" target="_blank" >RIV/61988987:17610/25:A2603BTE - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/10.1007/978-3-032-00891-6_17" target="_blank" >https://link.springer.com/10.1007/978-3-032-00891-6_17</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-032-00891-6_17" target="_blank" >10.1007/978-3-032-00891-6_17</a>
Alternative languages
Result language
angličtina
Original language name
Probabilistic-Fuzzy Inference with Piecewise Linear Quantile Regression
Original language description
Rule-based systems, particularly those using "IF antecedents THEN consequent" rules, play a crucial role in mathematical modeling and decision-making. This work focuses on a specific type of rule-based system: the Probabilistic-Fuzzy Inference System. Here, antecedents are represented as fuzzy sets, while consequents are modeled as probability distributions using quantile functions. The inference process relies on the $L_{1}$-Fuzzy transform, also known as the Quantile Fuzzy transform. For each fuzzy antecedent, a weighted quantile of order $p$ is computed, and the inverse quantile transform derives the empirical quantile function for any input value. Initially, weighted quantiles are modeled as scalar values. To better capture dependencies between input and output data, we extend this model to a piecewise linear function, developed in a structured manner with a comprehensive computational algorithm. Its effectiveness is demonstrated through an illustrative example.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Modeling Decisions for Artificial Intelligence
ISBN
978-3-032-00891-6
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
13
Pages from-to
214-226
Publisher name
Springer
Place of publication
Cham
Event location
València
Event date
Sep 15, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001585667300017