Design of Fuzzy Cognitive Maps for Modeling Time Series

Władysław Homenda , Agnieszka Jastrzębska , Witold Pedrycz

Abstract

This study elaborates on a comprehensive design methodology of Fuzzy Cognitive Maps (FCMs). Here the maps are regarded as a modeling vehicle of time series. It is apparent that whereas time series are predominantly numeric, FCMs are abstract constructs operating at the level of abstract entities referred to as concepts and represented by the individual nodes of the map. We introduce a mechanism to represent a numeric time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, which, in turn, gives rise to a collection of concepts forming the corresponding nodes of the FCMs. Each information granule is mapped onto a node (concept) of the map. We identify two fundamental design phases of FCMs, namely (a) formation of information granules mapping numeric data (time series) into activation levels of information granules (viz. nodes of the map), and (b) optimization of information granules at the parametric level, viz. learning (estimating) the weights between the nodes of the map. The learning is typically realized in a supervised mode on a basis of some experimental data. A construction of information granules is realized with the aid of fuzzy clustering, namely Fuzzy C-Means. The optimization is realized with the use of Particle Swarm Optimization (PSO). The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.
Author Władysław Homenda ZSMPW
Władysław Homenda,,
- Department of Structural Methods for Knowledge Processing
, Agnieszka Jastrzębska ZSMPW
Agnieszka Jastrzębska,,
- Department of Structural Methods for Knowledge Processing
, Witold Pedrycz
Witold Pedrycz,,
-
Journal seriesIEEE Transactions on Fuzzy Systems, ISSN 1063-6706
Issue year2016
Vol24
No1
Pages120-130
Publication size in sheets0.5
Keywords in Englishinternal and external optimization, fuzzy cognitive maps, information granules, fuzzy clustering, prediction, reconstruction error
Abstract in PolishW pracy przedstawiono metodykę modelowania szeregów czasowych na poziomie pojęć za pomocą rozmytych map poznawczych. Wprowadzono nowe sposoby konstrukcji mapy oraz zbadano alternatywne podejście do uczenia modelu. Zaproponowane podejście zostało zastosowane w serii eksperymentów empirycznych. W pracy opisane zostały uzyskane wyniki.
DOIDOI:10.1109/TFUZZ.2015.2428717
URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7100913
Languageen angielski
Score (nominal)50
ScoreMinisterial score = 50.0, 28-11-2017, ArticleFromJournal
Ministerial score (2013-2016) = 50.0, 28-11-2017, ArticleFromJournal
Publication indicators WoS Impact Factor: 2016 = 7.671 (2) - 2016=8.29 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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