Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

Shao Fei Jiang; Zhao Qi Wu

doi:10.4028/www.scientific.net/AMR.163-167.2482

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 163-167Effect of Attribute Reduction on...

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

Abstract:

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

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Periodical:

Advanced Materials Research (Volumes 163-167)

Pages:

2482-2487

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.163-167.2482

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Online since:

December 2010

Authors:

Shao Fei Jiang, Zhao Qi Wu

Keywords:

Attribute Reduction, Damage Identification, Probabilistic Neural Network (PNN), Rough Set (RS)

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