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Application of Genetic Optimization of Neural Network Method in the Stability Analysis of Open Pit Slope

机译:神经网络遗传优化在露天矿边坡稳定性分析中的应用

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Genetic algorithms and neural networks are combined to establish hybrid GA-BP algorithm in order to solve the problems in network training by pure BP algorithm, such as slow constringency, lack of theoretical guidance in network architecture design, as well as easy to fall into local minimum during the learning process. With the advantages of global optimization ability of genetic algorithm and the rapid constringency of the BP fast local searching algorithm, the comprehensive algorithm shows the ability of nonlinear approach of multilayer feed forward network, improves the performance of BP, and provides an efficient and practical method of slope stability analysis. The results show that the method can effectively and accurately predict the slope landslide and provide an available method for further evaluation of the slope stability.
机译:遗传算法和神经网络相结合建立混合GA-BP算法,以解决纯BP算法训练网络收敛速度慢,网络架构设计缺乏理论指导,易于陷入本地化等问题。在学习过程中最少。该综合算法利用遗传算法的全局优化能力和BP快速局部搜索算法的快速收敛性,展现了多层前馈网络的非线性方法的能力,提高了BP的性能,提供了一种高效实用的方法边坡稳定性分析。结果表明,该方法可以有效,准确地预测边坡滑坡,为进一步评价边坡稳定性提供了一种可行的方法。

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