Abstract
In this paper, learning of the FCMs represented using triangular fuzzy numbers (TFNs) in their weight matrices is studied. For this aim a population based novel learning approach is proposed. In the proposed algorithm, BB-BC optimization method is preferred because of its fast convergence capability. Moreover, this proposed approach involves concept by concept (CbC) learning to increase the accuracy of the learning of FCMs. Two different tests are realized as case studies for investigating the performance of the learning approach. For the first test, the learning capability of the algorithm is examined and for the second test the performance of generalization capability is investigated. The tests, which are presented via tables and figures, show that learning approach is successful for learning of FCMs with TFNs. Furthermore, from the case study it can be seen that the uncertain information can be represented and interpreted by the proposed FCM design methodology in a more efficient way.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015) |
Place of Publication | Piscataway, NJ. |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Print) | 9781467374286 |
DOIs | |
Publication status | Published - 25 Nov 2015 |
Event | IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey Duration: 2 Aug 2015 → 5 Aug 2015 |
Conference
Conference | IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 2/08/15 → 5/08/15 |
Keywords
- causal Links
- fuzzy cognitive maps
- learning
- reasoning
- triangular fuzzy numbers
- weight matrix