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Teng M, Pedrycz W. Knowledge transfer in project-based organisations: A dynamic granular cognitive maps approach. KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE 2021. [DOI: 10.1080/14778238.2021.1982421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ma Teng
- School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, China
| | - Witold Pedrycz
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada
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Dynamics of Fuzzy-Rough Cognitive Networks. Symmetry (Basel) 2021. [DOI: 10.3390/sym13050881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools.
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Priya M, Aswani Kumar C. An approach to merge domain ontologies using granular computing. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00193-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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