scholarly journals On extension of fuzzy measures to aggregation functions

2011 ◽  
Author(s):  
Anna Kolesarova ◽  
Andrea Stupnanova ◽  
Juliana Beganova
Keyword(s):  
Fuzzy Measures ◽  
Aggregation Functions

scholarly journals On Some Properties of Aggregation-Based Extensions of Fuzzy Measures

2018 ◽  
Vol 72 (1) ◽  
pp. 43-54
Author(s):  
L’ubomíra Horanská
Keyword(s):  
Fuzzy Measures ◽  
Aggregation Functions

Abstract In this paper, we analyse properties of aggregation-based extensions of fuzzy measures depending on properties of aggregation functions which they are based on. We mainly focus on properties possessed by the well-known Lovász and Owen extensions. Moreover, we characterize aggregation functions suitable for extension of particular subclasses of fuzzy measures.

scholarly journals A fuzzy-based framework to support multicriteria design of mechatronic systems

2020 ◽  
Vol 7 (6) ◽  
pp. 816-829 ◽  
Author(s):  
Abolfazl Mohebbi ◽  
Sofiane Achiche ◽  
Luc Baron
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Multicriteria Decision Making ◽  
Mechatronic System ◽  
Mechatronic Systems ◽  
Fuzzy Measures ◽  
Aggregation Functions ◽  
Decision Outcomes ◽  
Multiobjective Design

Abstract Designing a mechatronic system is a complex task since it deals with a high number of system components with multidisciplinary nature in the presence of interacting design objectives. Currently, the sequential design is widely used by designers in industries that deal with different domains and their corresponding design objectives separately leading to a functional but not necessarily an optimal result. Consequently, the need for a systematic and multiobjective design methodology arises. A new conceptual design approach based on a multicriteria profile for mechatronic systems has been previously presented by the authors, which uses a series of nonlinear fuzzy-based aggregation functions to facilitate decision-making for design evaluation in the presence of interacting criteria. Choquet fuzzy integrals are one of the most expressive and reliable preference models used in decision theory for multicriteria decision-making. They perform a weighted aggregation by the means of fuzzy measures assigning a weight to any coalition of criteria. This enables the designers to model importance and also interactions among criteria, thus covering an important range of possible decision outcomes. However, specification of the fuzzy measures involves many parameters and is very difficult when only relying on the designer's intuition. In this paper, we discuss three different methods of fuzzy measure identification tailored for a mechatronic design process and exemplified by a case study of designing a vision-guided quadrotor drone. The results obtained from each method are discussed in the end.

Aggregation Functions, Similarity and Fuzzy Measures

2017 ◽  
pp. 223-228 ◽  
Author(s):  
Surajit Borkotokey ◽  
Magdaléna Komorníková ◽  
Jun Li ◽  
Radko Mesiar
Keyword(s):  
Fuzzy Measures ◽  
Aggregation Functions

k–Order Fuzzy Measures and k–Order Aggregation Functions

2019 ◽  
pp. 193-203
Author(s):  
Gleb Beliakov ◽  
Simon James ◽  
Jian-Zhang Wu
Keyword(s):  
Fuzzy Measures ◽  
Aggregation Functions

Special fuzzy measures on infinite countable sets and related aggregation functions

2011 ◽  
Vol 167 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Andrea Stupňanová
Keyword(s):  
Fuzzy Measures ◽  
Aggregation Functions

Aggregation Functions

2009 ◽  
Author(s):  
Michel Grabisch ◽  
Jean-Luc Marichal ◽  
Radko Mesiar ◽  
Endre Pap
Keyword(s):  
Aggregation Functions

AGGREGATION FUNCTIONS WITH NON-MONOTONIC MEASURES

2008 ◽  
Vol 13 (02) ◽  
Author(s):  
Marta Cardin ◽  
Silvio Giove
Keyword(s):  
Aggregation Functions

Characterization of homogeneous and quasi-homogeneous binary aggregation functions

2021 ◽  
Author(s):  
Yong Su ◽  
Wenwen Zong ◽  
Radko Mesiar
Keyword(s):  
Aggregation Functions

scholarly journals Unsupervised Offline Changepoint Detection Ensembles

2021 ◽  
Vol 11 (9) ◽  
pp. 4280
Author(s):  
Iurii Katser ◽  
Viacheslav Kozitsin ◽  
Victor Lobachev ◽  
Ivan Maksimov
Keyword(s):  
Numerical Experiment ◽  
Failure Time ◽  
Cost Functions ◽  
Technical Diagnostics ◽  
Classification Problems ◽  
Tennessee Eastman Process ◽  
Changepoint Detection ◽  
Aggregation Functions ◽  
The Individual ◽  
Ensemble Algorithms

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

Fuzzy Measures and Choquet Integrals based on Fuzzy Covering Rough Sets

2021 ◽  
pp. 1-1
Author(s):  
Xiaohong Zhang ◽  
Jingqian Wang ◽  
Jianming Zhan ◽  
Jianhua Dai
Keyword(s):  
Rough Sets ◽  
Fuzzy Measures ◽  
Choquet Integrals
Sign in / Sign up

Export Citation Format

Share Document