基于遗传算法的地下水位动态预测双线性模型

A Bilinear Time Series Model Based on Genetic Algorithm for Predicting Groundwater Level Regime

提出了建立双线性模型（ＢＭ）的一套简便通用的方法。用加速遗传算法可同时估计ＢＭ 模型各参数，成功地解决了ＢＭ建模这一难题，为ＢＭ模型的广泛应用提供了新工具。实例计算 结果说明：用这套方法预测地下水位动态是可行而有效的；通过利用预测过程中产生的残差信 息进行反馈矫正，保证了ＢＭ模型劫高的拟合精度和稳健的预测性能，增强了对复杂非线性动态 系统的适应性。

A simple and universal scheme is presented for establishing the bilinear time series model (BM). All parameters of the BM can be optimized at the same time with the accelerating genetic algorithm developed by the authors, and the difficulty of modeling the BM is resolved, which gives a new tool for widely applying the BM. The examples show that the scheme applied to predicting groundwater level regime is practical and efficient, and the BM can ensure high fitting precision, robust forecasting and good adaptability to complex nonlinear dynamic systems by using the feedback information of prediction residual errors. As a general method, the scheme can be applied to the forecasting of nonlinear time series in various engineering practices.