Optimal Fuzzy Models with the Aid of SAHN-based Algorithm | Zendy

JongSeok Lee | Zendy; KyungWon Jang | Zendy; Tae-Chon Ahn | Zendy

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Optimal Fuzzy Models with the Aid of SAHN-based Algorithm

Author(s) -

JongSeok Lee,

KyungWon Jang,

Tae-Chon Ahn

Publication year - 2006

Publication title -

international journal of fuzzy logic and intelligent systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.296

H-Index - 9

eISSN - 2093-744X

pISSN - 1598-2645

DOI - 10.5391/ijfis.2006.6.2.138

Subject(s) - fuzzy logic , computer science , cluster analysis , data mining , algorithm , fuzzy clustering , identification (biology) , fuzzy set operations , cluster (spacecraft) , adaptive neuro fuzzy inference system , process (computing) , fuzzy set , artificial intelligence , fuzzy control system , botany , biology , programming language , operating system

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

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