4.7 Article

An improved type-reduction algorithm for general type-2 fuzzy sets

期刊

INFORMATION SCIENCES
卷 593, 期 -, 页码 99-120

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.01.078

关键词

General type-2 fuzzy sets; Type reduction; a-Plane representation; Improved enhanced opposite direction searching (IEODS)

资金

  1. National Science Foundation of China (NSFC) [61773016, 62073259, 61903298]

向作者/读者索取更多资源

This paper proposes a novel and efficient method for centroid type reduction in general type-2 fuzzy systems. The method is based on alpha-plane representation and an improved enhanced opposite direction searching algorithm. Experimental results demonstrate that the method outperforms other alpha-plane representation-based reduction methods in terms of calculation time and iterations. By improving type reduction efficiency, the method enhances the applicability of general type-2 fuzzy systems in complex environments and embedded platforms.
General type-2 fuzzy systems (GT2 FLSs) provide a more flexible way of overcoming an uncertain lack of uniformity in different applications. Centroid type reduction is one of the major component of GT2 FLSs, it is currently one of the key factors restricting GT2 FLSs efficiency. This paper proposes a novel and efficient method for centroid type reduction. The method is based on alpha-plane representation, where a general type-2 fuzzy set is decomposing into a series of alpha planes. The centroid for each plane is the calculated, layer by layer, from the top down, until the alpha = 0 plane is reached. In each alpha plane, a centroid type reduction calculation is performed using an improved enhanced opposite direction searching algorithm (IEODS). Finally, the centroids obtained for each plane are aggregated to obtain a type-1 fuzzy set, which form the centroid of general type-2 fuzzy set. Experiments show that this method results in less calculation time and fewer iterations than other alpha-plane representation-based reduction methods. By providing more efficient type reduction, the method improves the applicability of general type-2 fuzzy systems in more complex environments and embedded platforms, thereby enhancing their scope for future development. (c) 2022 Elsevier Inc. All rights reserved.

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