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Author:

Xie, Wei (Xie, Wei.) | Duan, Jian-Min (Duan, Jian-Min.) (Scholars:段建民)

Indexed by:

CPCI-S

Abstract:

Structural safety monitoring system uses an array of sensors to continuously monitor a structure to provide an early indication of problems such as damage to the structure from fatigue, corrosion or impact. The system is a large sensor network containing about two hundred nodes, each of which contains multiple sensors. So the sensor fault diagnosis is getting more and more important. A fuzzy-neural method is suggested in this paper. Based on the function equivalence between T-S fuzzy inference and RBF network, a kind of fuzzy neural controller based on RBF network with full-net structure is put forward. This paper proposes a real code GA to optimize all factors including scaling factors, membership functions and fuzzy rules. The RBF network with two inputs-one output model is used as fuzzy controller, the result of the simulation illustrates that the controller has good dynamic performance and strong robust.

Keyword:

GA RBF network Safety monitoring system T-S fuzzy inference

Author Community:

  • [ 1 ] [Xie, Wei]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Duan, Jian-Min]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Xie, Wei]Beijing Polytech Univ, Beijing 100176, Peoples R China

Reprint Author's Address:

  • [Xie, Wei]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

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Source :

MACHINERY, MATERIALS SCIENCE AND ENERGY ENGINEERING

Year: 2015

Page: 633-641

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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