欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了提高径流预报的精度，采用一种基于粒子群和遗传的混合方法同时优化人工神经网络结构、连接权和偏置，在进化过程中采用训练样本和验证样本共享适应度技术，并以此建立径流预报模型。通过对柳州径流实例分析，并与离子群优化的人工神经网络模型、遗传进化的人工神经网络模型和时间序列模型方法对比，研究结果表明，该方法学习能力强、泛化性能高和有效提高系统预测的准确率，为获得更高预测精度的径流预报提供了一种有效的建模方法。
英文摘要：
In order to improve the accuracy of runoff forecasting, a hybrid algorithm combining PSO and GA algorithm with optimizing artificial neural network structure, connection weights and bias was proposed and used to establish a runoff forecasting model. This hybrid algorithm adopts training samples and validation samples to share fitness in the evolutionary process. The algorithm was com-pared with two forecasting models including PSO-ANN and GA-ANN through the actual examples of Liuzhou runoff forecasting. The results show that the new approach has strong learning ability and high generalization performance and can improve the accuracy of forecasting system effectively. Thus, it is an effective modeling method to get high precision of runoff forecasting.
同期刊论文项目
同项目期刊论文
Dynamic modeling and response analysis of lateral-torsional coupling vibration of the slewing mechan
Evolving Neural Network Using Hybrid Genetic Algorithm and Simulated Annealing for Rainfall-Runoff F
EvolvingRBF neural networks for rainfall prediction using hybrid particle swarmoptimization and gene
Hybrid of Genetic Algorithmand Simulated Annealing for Support Vector Regression Optimization inRain
期刊信息
位置：