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首页> 外文会议>2010 International Conference on Intelligent Computation Technology and Automation >PSO-BP Neural Network Model for Predicting Water Temperature in the Middle of the Yangtze River
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PSO-BP Neural Network Model for Predicting Water Temperature in the Middle of the Yangtze River

机译：长江中游水温预测的PSO-BP神经网络模型

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摘要

River temperature prediction is an Important project in the environmental impact assessments. Based on river temperature data of Yichang hydrological station in the middle reach of the Yangtze River, BP neural network model based on particle swarm optimization (PSO) was applied to predict river temperature of the Yangtze River. PSO was used to optimize the initial weights of nodes in BP neural network and overcome the over-fitting problem and the local minima problem of the BP neural network. MATLAB was applied to simulate the model. The results show that the prediction precision was improved greatly and the model had better generalization performance. The study proved that PSO-BP neural network model was effective in river temperature prediction.
机译：河流温度预测是环境影响评估中的重要项目。基于长江中游宜昌水文站的河温数据，应用基于粒子群优化（PSO）的BP神经网络模型对长江水温进行了预测。 PSO用于优化BP神经网络中节点的初始权重，并克服了BP神经网络的过拟合问题和局部极小问题。应用MATLAB对该模型进行仿真。结果表明，该模型的预测精度大大提高，模型具有更好的泛化性能。研究证明，PSO-BP神经网络模型在河道温度预测中是有效的。

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