Type-Reduction of General Type-2 Fuzzy Sets: The Type-1 OWA Approach
| dc.contributor.author | Chiclana, Francisco | en |
| dc.contributor.author | Zhou, Shang-Ming | en |
| dc.date.accessioned | 2013-03-25T15:25:24Z | |
| dc.date.available | 2013-03-25T15:25:24Z | |
| dc.date.issued | 2013-05 | |
| dc.description | Other Research Group involved in the research: CCI | en |
| dc.description.abstract | For general type-2 fuzzy sets, the defuzzification process is very complex, and the exhaustive direct method of implementing type-reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type-2 fuzzy inferencing systems in real world applications. The present situation will not be expected to change, unless an efficient and fast method of deffuzzifying general type-2 fuzzy sets emerges. Type-1 Ordered Weighted Averaging (OWA) operators have been proposed to aggregate expert uncertain knowledge expressed by type-1 fuzzy sets in decision making. In particular, the recently developed Alpha-Level Approach to type-1 OWA operations has proven to be an effective tool for aggregating uncertain information with uncertain weights in real-time applications because its complexity is of linear order. In this paper, we prove that the mathematical representation of the type-reduced set (TRS) of a general type-2 fuzzy set is equivalent to that of a special case of type-1 OWA operator. This relationship opens up a new way of performing type-reduction of general type-2 fuzzy sets, allowing the use of the Alpha-Level Approach to type-1 OWA operations to compute the TRS of a general type-2 fuzzy set. As a result, a fast and efficient method of computing the centroid of general type-2 fuzzy sets is realised. The experimental results presented here illustrate the effectiveness of this method in conducting type-reduction of different general type-2 fuzzy sets. | en |
| dc.identifier.citation | Chiclana, F. and Zhou, S.M. (2013) Type-Reduction of General Type-2 Fuzzy Sets: The Type-1 OWA Approach. International Journal of Intelligent Systems, 28 (5), pp. 505-522 | en |
| dc.identifier.doi | https://doi.org/10.1002/int.21588 | |
| dc.identifier.uri | http://hdl.handle.net/2086/8294 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | Wiley | en |
| dc.researchgroup | DIGITS | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Type-1 OWA operator | en |
| dc.subject | Type-2 fuzzy sets | en |
| dc.subject | Alpha-level | en |
| dc.subject | Type-reduced set | en |
| dc.subject | Representation theorem | en |
| dc.title | Type-Reduction of General Type-2 Fuzzy Sets: The Type-1 OWA Approach | en |
| dc.type | Article | en |
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