基于GA-GRNN的地表下沉系数预测方法研究

doi:10.11799/ce201407031

Abstract

Abstract:

A new method by combining Genetic Algorithm (GA) and Generalized Regression Neural Network (GRNN) is presented. In this method, the GA is used to optimize the smoothing parameter of GRNN. An intelligent prediction model for surface subsidence coefficient using this hybrid GA-GRNN algorithm is constructed based on the analysis of impact factors. Typical data of surface moving observation stations is used as learning and test samples. Comparison analysis is made between predicted values generated by GA-GRNN method and measured values. Results indicate that this model could make use of multi-factor. The maximum relative error between the predicted results and the measured is only 5.44%, which is fully meet the needs of field engineering. A new approach for the future prediction of surface subsidence coefficient is proposed.

Key words: Genetic Algorithm, Generalized Regression Neural Network, surface subsidence coefficient, mining subsidence

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URL: http://www.coale.com.cn/EN/10.11799/ce201407031

http://www.coale.com.cn/EN/Y2014/V46/I7/94

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Study on the prediction method of surface subsidence coefficient using GA-GRNN

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