关键词: 遗传算法, 广义回归神经网络, 地表下沉系数, 开采沉陷

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