隧道围岩破坏模式的进化神经网络识别

隧道围岩破坏受很多因素的影响，其破坏模式的识别是一个复杂的非线性系统辨识问题，采用一般方法很难得到好的解答。基于作者提出的免疫进化规划，并把它同神经网络(NN)相结合，提出了一种全新的结构及权值同时进化的进化神经网络(ENN)模型，用于围岩破坏模式的识别研究，用一个试验算例证明了进化神经网络具有解决此问题的良好性能。

Identification of collapse type of surrounding rock mass of tunnels using evolutionary neural network

The collapse of surrounding rock mass of tunnels is affected by many factors; the identification of the collapse type is a very complicated nonlinear system identification and it can not be solved by traditional methods. The problem of complicated nonlinear system identification can be solved very well using neural network (NN) model. Considering the existing problems of the traditional NN model and traditional evolutionary neural network(ENN) model and combining the immune evolutionary programming (IEP) proposed by authors with NN, a new ENN model whose architecture and connection weights evolve simultaneously is proposed. Using this new ENN model, the problem to identify the collapse type of surrounding rock mass of tunnel is studied. The results of an example show that the performance of the new ENN is very well and it is a very good method to identify the collapse type of surrounding rock mass of tunnels.