CAJ | 학술논문

大坝变形预测是风险评估的关键，而涉及因素存在高度非线性。为达到好的预测效果，提出了一种基于最小二乘支持向量机（LSSVM）的大坝变形预测方法。在数据预处理方面，针对传统的参数平方、立方这种处理方式，提出变阶次概念；针对LSSVM交叉验证耗时过多，提出了一种简单可行的变参数方法。为了快速获得优化结果，引入基于十进制的遗传算法。此外，为进一步提高预测精度，引入遗忘因子概念。最后，给出一个实例。
대패변형예측시풍험평고적관건，이섭급인소존재고도비선성。위체도호적예측효과，제출료일충기우최소이승지지향량궤（LSSVM）적대패변형예측방법。재수거예처리방면，침대전통적삼수평방、립방저충처리방식，제출변계차개념；침대LSSVM교차험증모시과다，제출료일충간단가행적변삼수방법。위료쾌속획득우화결과，인입기우십진제적유전산법。차외，위진일보제고예측정도，인입유망인자개념。최후，급출일개실례。
Dam deformation prediction is the key to the risk assessment,and the involved factors are highly nonlinear. In the paper,to achieve better prediction effects,a method of dam deformation prediction based on least squares support vector machine(LSSVM)is proposed. First,the traditional transaction way of the square and the cubic of the water level is changed by introducing the concept of variable orders. Second,to solve the time-consuming problem,a simple and feasible method of variable parameters is suggested to escape the cross validation of LSSVM. Third ,in order to get the optimization results quickly,the genetic algorithm based on decimal system is adopted. In addition, the concept of forgetting factor is used to improve the prediction accuracy further. Finally ,an example is given.