Application of neural network based on simulated annealing Gauss-Newton algorithm to seepage back analysis
Application of neural network based on simulated annealing Gauss-Newton algorithm to seepage back analysis
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发表时间：
2005
影响因子：
1.5
通讯作者：
Zhang Li-jun
中科院分区：
文献类型：
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作者：
Zhang Li-jun
提出了应用于神经网络的模拟退火高斯-牛顿算法。它克服了传统BP神经网络的一些局限性。以正弦函数的迭代收敛为例，证明了该算法的正确性、有效性和优越性。同时，将该算法应用于铜乐坪大坝渗流反分析，利用渗流系数计算渗流场。预测水头与观测值接近，说明反分析结果是正确的，该算法在实际渗流参数识别中是可行的。
Simulated Annealing Gauss-Newton algorithm applied to neural network is put forward. It overcomes some limitation of the traditional BP neural network. Taking iterative convergence of sine function for example, the correction, efficiency and superiority of the algorithm are proved. At the same time, this algorithm is applied to seepage back analysis of Tongleping dam, using seepage coefficient to calculate seepage flow field. And forecasted water heads approach to the observed values, which illuminates the back analysis result is correct and the algorithm is feasible in the practical seepage parameters identification.