2,022 research outputs found
OWA operators in the calculation of the average green-house gases emissions
This study proposes, through weighted averages and ordered weighted averaging operators, a new aggregation system for the investigation of average gases emissions. We present the ordered weighted averaging operators gases emissions, the induced ordered weighted averaging operators gases emissions, the weighted ordered weighted averaging operators gases emissions and the induced probabilistic weighted ordered weighted averaging operators gases emissions. These operators represent a new way of analyzing the average gases emissions of different variables like countries or regions. The work presents further generalizations by using generalized and quasi-arithmetic means. The article also presents an illustrative example with respect to the calculations of the average gases emissions in the European region
Fuzzy decision making in complex frameworks with generalized aggregation operators
[EN] This article presents a new aggregation system applied to fuzzy decision making. The fuzzy generalized unified aggregation operator (FGUAO) is a system that integrates many operators by adding a new aggregation process that considers the relevance that each operator has in the analysis. It also deals with an uncertain environment where the information is studied with fuzzy numbers. A wide range of particular cases and properties are studied. This approach is further extended by using quasi-arithmetic means. The paper ends studying the applicability in decision making problems regarding the European Union decisions. For doing so, the work uses a multi-person aggregation process obtaining the multi-person - FGUAO operator. An example concerning the fixation of the interest rate by the European Central Bank is presented. (C) 2018 Elsevier B.V. All rights reserved.We would like to thank the associate editor and the anonymous reviewers for valuable comments that have improved the quality of the paper. Support from the Chilean Government through the Fondecyt Regular program (project number 1160286), the University of Chile, the project PIEF-GA-2011-300062 of the European Commission and the Distinguished Scientist Fellowship Program of the King Saud University (Saudi Arabia), is gratefully acknowledged.Merigó -Lindahl, JM.; Gil-Lafuente, AM.; Yu, D.; Llopis Albert, C. (2018). Fuzzy decision making in complex frameworks with generalized aggregation operators. Applied Soft Computing. 68:314-321. https://doi.org/10.1016/j.asoc.2018.04.002S3143216
Cycle flows and multistabilty in oscillatory networks: an overview
The functions of many networked systems in physics, biology or engineering
rely on a coordinated or synchronized dynamics of its constituents. In power
grids for example, all generators must synchronize and run at the same
frequency and their phases need to appoximately lock to guarantee a steady
power flow. Here, we analyze the existence and multitude of such phase-locked
states. Focusing on edge and cycle flows instead of the nodal phases we derive
rigorous results on the existence and number of such states. Generally,
multiple phase-locked states coexist in networks with strong edges, long
elementary cycles and a homogeneous distribution of natural frequencies or
power injections, respectively. We offer an algorithm to systematically compute
multiple phase- locked states and demonstrate some surprising dynamical
consequences of multistability
Ordinal and nominal classication of wind speed from synoptic pressure patterns
Wind speed reconstruction is a challenging problem in areas (mainly wind
farms) where there are not direct wind measures available. Di erent approaches
have been applied to this reconstruction, such as measure-correlatepredict
algorithms, approaches based on physical models such as reanalysis
methods, or more recently, indirect measures such as pressure, and its relation
to wind speed. This paper adopts the latter method, and deals with wind
speed estimation in wind farms from pressure measures, but including different
novelties in the problem treatment. Existing synoptic pressure-based
indirect approaches for wind speed estimation are based on considering the
wind speed as a continuous target variable, estimating then the corresponding
wind series of continuous values. However, the exact wind speed is not
always needed by wind farms managers, and a general idea of the level of
speed is, in the majority of cases, enough to set functional operations for the
farm (such as wind turbines stop, for example). Moreover, the accuracy of the models obtained is usually improved for the classi cation task, given that
the problem is simpli ed. Thus, this paper tackles the problem of wind speed
prediction from synoptic pressure patterns by considering wind speed as a
discrete variable and, consequently, wind speed prediction as a classi cation
problem, with four wind level categories: low, moderate, high or very high.
Moreover, taking into account that these four di erent classes are associated
to four values in an ordinal scale, the problem can be considered as an ordinal
regression problem. The performance of several ordinal and nominal classi-
ers and the improvement achieved by considering the ordering information
are evaluated. The results obtained in this paper present the Support Vector
Machine as the best tested classi er for this task. In addition, the use of
the intrinsic ordering information of the problem is shown to signi cantly
improve ranks with respect to nominal classi cation, although di erences in
accuracy are smal
Community detection for correlation matrices
A challenging problem in the study of complex systems is that of resolving,
without prior information, the emergent, mesoscopic organization determined by
groups of units whose dynamical activity is more strongly correlated internally
than with the rest of the system. The existing techniques to filter
correlations are not explicitly oriented towards identifying such modules and
can suffer from an unavoidable information loss. A promising alternative is
that of employing community detection techniques developed in network theory.
Unfortunately, this approach has focused predominantly on replacing network
data with correlation matrices, a procedure that tends to be intrinsically
biased due to its inconsistency with the null hypotheses underlying the
existing algorithms. Here we introduce, via a consistent redefinition of null
models based on random matrix theory, the appropriate correlation-based
counterparts of the most popular community detection techniques. Our methods
can filter out both unit-specific noise and system-wide dependencies, and the
resulting communities are internally correlated and mutually anti-correlated.
We also implement multiresolution and multifrequency approaches revealing
hierarchically nested sub-communities with `hard' cores and `soft' peripheries.
We apply our techniques to several financial time series and identify
mesoscopic groups of stocks which are irreducible to a standard, sectorial
taxonomy, detect `soft stocks' that alternate between communities, and discuss
implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR
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