An Effective Hybrid Approach for Processing Deformation Monitoring Data

Ning Gao; Xi Min Cui; Cai Yun Gao

doi:10.4028/www.scientific.net/AMR.446-449.3247

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 446-449An Effective Hybrid Approach for Processing...

An Effective Hybrid Approach for Processing Deformation Monitoring Data

Abstract:

This paper describes the procedure of a hybrid approach based on grey model and artificial neural network (GM&ANN) to analysis and forecast of deformation data. The GM&ANN is formulated into three steps:(1)according to the monotonously increasing characteristics and the nonlinear characteristics of deformation time series, total deformation can be divided into tendency part and stochastic part.(2) use GM(1,1)to fit the trend of the data and obtain the residual series, on this basis by using artificial neural network to fit the stochastic part (residual series) .Then the forecasting value of deformation is obtained by adding the calculated predictive displacement value of each sub-stack. (3) validate the model. The results of experiments show that this hybrid has higher performances not only on model fitting but also on forecasting and therefore can be applied to deformation data processing.

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Periodical:

Advanced Materials Research (Volumes 446-449)

Pages:

3247-3251

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.446-449.3247

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Online since:

January 2012

Authors:

Ning Gao, Xi Min Cui, Cai Yun Gao

Keywords:

Artificial Neural Network (ANN), Deformation, Grey Model, Hybrid Forecasting Model, Residual Error Modification

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