期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 24, 期 1, 页码 120-130出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2015.2428717
关键词
Fuzzy clustering; fuzzy cognitive maps (FCMs); information granules; internal and external optimization; prediction; reconstruction error
资金
- National Science Center [2011/01/B/ST6/06478, UMO-2011/01/B/ST6/06478]
- Natural Sciences and Engineering Research Council of Canada
- Canada Research Chair Program
- Foundation for Polish Science under International PhD Projects in Intelligent Computing
- European Union
- European Regional Development Fund
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 1) formation of information granules mapping numeric data (time series) into activation levels of information granules (viz., the nodes of the map), and 2) 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. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.
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