多组群教学优化算法-神经网络-支持向量机组合模型在径流预测中的应用

采用5个标准测试函数对多组群教学优化(MGTLO)算法进行仿真验证,并将仿真结果与基本教学优化(TLBO)算法、混合蛙跳算法(SFLA)、差分进化(DE)算法和粒子群优化(PSO)算法的仿真结果进行对比。利用MGTLO算法搜寻基于广义回归神经网络(GRNN)、径向基神经网络(RBF)、支持向量机(SVM)模型单元的组合模型的最佳模型参数和组合权重系数,提出MGTLO-GRNN-RBF、MGTLO-GRNN-SVM、MGTLO-RBF-SVM、MGTLO-GRNN-RBF-SVM 4种组合预测模型,以新疆伊犁河雅马渡水文站和云南省某水文站年径流量预测为例进行了实例分析,并将预测结果与MGTLO-GRNN、MGTLO-RBF、MGTLO-SVM和GRNN、RBF、SVM 6种单一模型的结果进行对比分析。结果表明:MGTLO算法寻优精度优于TLBO、SFLA、DE和PSO算法,具有较好的收敛速度和全局极值寻优能力;组合模型融合了MGTLO算法与GRNN、RBF、SVM模型单元的优点,在预测精度、泛化能力等方面均优于单一模型;MGTLO算法能有效优化各组合模型的相关参数和权重系数,MGTLO-GRNN-RBF-SVM模型预测精度最高。

Combined model of multi-group teaching optimization algorithm-neural network-support vector machine in runoff prediction application

Five standard test functions were used to verify the multi-group teaching optimization(MGTLO)algorithm, and the simulation results were compared with those of the basic teaching optimization(TLBO)algorithm, shuffled frog leaping algorithm(SFLA), differential evolution(DE)algorithm and particle swarm optimization(PSO)algorithm. MGTLO was used to search for the optimum model parameters and the weight coefficients of the combined model based on the generalized regression neural network(GRNN), the radial basis function neural network(RBF)and the support vector machine(SVM)model elements. Four combined prediction models, including MGTLO-GRNN-RBF, MGTLO-GRNN-SVM, MGTLO-RBF-SVM, and MGTLO-GRNN-RBF-SVM were proposed and case studies of the runoff prediction were performed at the Yamadu Hydrological Station of the Yili River in Xinjiang and a hydrological station in Yunnan Province. The predicted results were compared with the following six single models, MGTLO-GRNN, MGTLO-RBF, MGTLO-SVM, GRNN, RBF and SVM. The results show that the optimization accuracy of MGTLO algorithm is better than that of TLBO, SFLA, DE and PSO, with good convergence speed and global optimization ability. The combined model merges the advantages of MGTLO algorithm, GRNN, RBF, and SVM model elements. It is superior to single models in terms of prediction accuracy and generalization ability. MGTLO algorithm can effectively optimize the parameters and weight coefficients of the combined models and the MGTLO-GRNN-RBF-SVM model has the highest prediction accuracy.