期刊
INFORMATION SCIENCES
卷 180, 期 24, 页码 5060-5076出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.03.023
关键词
Fuzzy inference systems (FIS); Genetic optimization; Granular data; Intervals number (IN); Lattice theory; Linear approximation; Rules; Self-organizing map (SOM); Similarity measure; Structure identification; TSK model
资金
- Archimedes-III
Linear models are preferable due to simplicity. Nevertheless, non-linear models often emerge in practice. A popular approach for modeling nonlinearities is by piecewise-linear approximation. Inspired from fuzzy inference systems (FISs) of Tagaki-Sugeno-Kang (TSK) type as well as from Kohonen's self-organizing map (KSOM) this work introduces a genetically optimized synergy based on intervals' numbers, or INs for short. The latter (INS) are interpreted here either probabilistically or possibilistically. The employment of mathematical lattice theory is instrumental. Advantages include accommodation of granular data, introduction of tunable nonlinearities, and induction of descriptive decision-making knowledge (rules) from the data. Both efficiency and effectiveness are demonstrated in three benchmark problems. The proposed computational method demonstrates invariably a better capacity for generalization; moreover, it learns orders-of-magnitude faster than alternative methods inducing clearly fewer rules. (C) 2010 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据