针对该课题，本文对边坡稳定性评价的神经网络方法及神经网络的遗传优化进行了探讨和研究。
As to this method, some researches about the slope stability evaluation by neural networks method and its optimizing means were done by this thesis.
由于动态神经网络结构及权值确定困难，采用二进制与实数编码相结合的联合编码，用遗传算法优化得到神经网络结构及对应权值。
To rise above the difficulty of determining NN's structure and weights, the GA optimization algorithm is used to get them by combining binary encoding with real encoding.
提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法。
A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient.
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