巷道围岩松动圈预测的进化神经网络法

针对简单遗传算法收敛速度慢、易早熟的缺点，从编码、初始群体生成及遗传算子等方面对其进行了改进，提出了一个收敛速度快、性能良好的算法，并通过仿真实验证明了该算法的性能。把该算法用于神经网络的结构及学习参数的进化学习，建立了一个进化神经网络模型。最后，用该模型对巷道围岩松动圈厚度进行了预测研究，结果证明，该进化神经网络模型拟合性能及推广预测性能均令人满意。

EVOLUTIONARY NEURAL NETWORK MODEL ON PREDICATION OF LOOSEN ZONE AROUND ROADWAY

In view of the shortcomings of the simple genetic algorithm(SGA), such as slow convergence and premature character, a new algorithm with fast convergence, good performance is proposed by improving the encoding, generation of initial population and genetic operators. And the new algorithm is tested and verified by experiments. An evolutionary neural network(ENN) model is constructed by using this algorithm to evolve the architecture and parameters of neural network. At last, the prediction on the thickness of the loosen zone around roadway is made with this ENN model, and the results show that the convergence and the forecasting capability are all good.