摘要
为提高年径流预测精度,利用同热传递搜索(SHTS)算法对混合核支持向量机(SVM)关键参数和混合权重系数进行优化,提出混合核SHTS-SVM年径流预测模型。通过6个不同维度的标准测试函数对SHTS算法进行仿真验证,并与当前寻优效果较好的教学优化(TLBO)算法、灰狼优化(GWO)算法进行对比验证。利用两个年径流预测算例对混合核SHTS-SVM模型进行实例验证,并与多项式核SHTS-SVM、高斯核SHTS-SVM及SHTS-BP模型预测结果进行对比。结果表明:SHTS算法寻优精度优于TLBO、GWO优化算法,具有较好的极值寻优能力和稳健性能。混合核SHTS-SVM模型综合了多项式全局核函数和高斯局部核函数二者优点,在预测精度、泛化能力等方面均优于对比模型,具有较好的实际应用价值。
In order to improve the accuracy of annual runoff prediction, the simultaneous heat transfer search (SHTS) algorithm was used to optimize the key parameters and the mixed weight coefficients of hybrid nuclear support vector machine (SVM). A mixed kernel SHTS-SVM annual runoff prediction model was proposed. The SHTS algorithm was verified by six standard test functions at different dimensions, and verified with the teaching optimization (TLBO) algorithm and gray wolf optimization (GWO) algorithm. Two of the annual runoff prediction examples were used to verify the mixed kernel SHTS-SVM model and compared with the prediction results of polynomial kernel SHTS-SVM, Gaussian kernel SHTS-SVM and SHTS-BP models. The results showed that the optimization accuracy of SHTS algorithm is better than the TLBO and GWO optimization algorithms, and it has better extreme value searching ability and robust performance. The mixed kernel SHTS-SVM model combines the advantages of the polynomial global kernel function and the Gaussian local kernel function. It is superior to the comparison model in terms of prediction accuracy, generalization ability and so on, and has good practical application value.
作者
周有荣
崔东文
ZHOU Yourong;CUI Dongwen(Lincang Runting Water Resources Technology Services Limited, Lincang 677000,China;Wenshan Water Bureau of Yunnan Province, Wenshan 663000,China)
出处
《水资源与水工程学报》
CSCD
2019年第3期66-72,共7页
Journal of Water Resources and Water Engineering
关键词
径流预测
同热传递搜索算法
混合核函数
支持向量机
参数优化
仿真验证
runoff forecasting
simultaneous heat transfer search(SHTS) algorithm
mixed kernels
support vector machine(SVM)
parameter optimization
simulation verification
