NDLI: T-S model identification based on silhouette index and improved gravitational search algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xueming Ding Zhenkai Xu Meimei Liu Juan Wu
Copyright Year	2014
Description	Author affiliation: Dept. of Control Sci. & Eng., Univ. of Shanghai for Sci. & Technol., Shanghai, China (Xueming Ding; Zhenkai Xu; Meimei Liu; Juan Wu)
Abstract	In this paper, an approach based on silhouette index (SI) and improved gravitational search algorithm (IGSA) is presented to deal with T-S model identification problem. Clustering algorithm employing SI and IGSA is introduced for structure identification. The SI considers both the intra-cluster cohesion and the inter-cluster separation which can highly assess the accuracy of clustering. One cluster represents a fuzzy rule. Cluster center is regarded as the gauss membership function center parameter, which is identified by IGSA. The improved algorithm IGSA is also used for parameter identification of T-S model. It introduces the mutation of genes to standard GSA and considers the best solution, which enhances the search space and improves the ability of sharing global information. The simulation results produced by a two order nonlinear system and Box-Jenkins gas stove illustrate the effectiveness of the proposed method.
Starting Page	4423
Ending Page	4428
File Size	148887
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6895681
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Accuracy clustering algorithm Clustering algorithms Data models Silicon Indexes gravitational search algorithm(GSA) Nonlinear systems T-S model Testing silhouette index(SI)
Content Type	Text
Resource Type	Article

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