改进的渗流参数反演方法及其工程应用

Improved Algorithm of Seepage Coefficient Inversion and Project Application

提出了模拟退火的交替迭代算法的神经网络方法,克服了经典BP网络存在的一些缺陷。并以正弦函数的迭代收敛为例,利用模拟退火的交替迭代算法反演了大坝的渗透系数,并利用反演结果进行渗流场分析,结果说明,水头预报值有很高的精度,反演结果是正确的,从而验证了该方法用于反演分析中的可靠性、有效性和优越性。

Simulated Annealing alternation and iterative algorithm applied in Neural Network is proposed forward in this paper, which 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 in seepage inversion of dam, using inversion results to calculate seepage flow field, and forecasted water heads have good accuracy. Inversion result is correct and the algorithm is feasible in the practical seepage parameters identification.