基于RBF神经网络-灰色系统联合模型的水库径流预测

Juan Zhang, Chang-jun Zhu
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引用次数: 2

摘要

目前,水库径流预测采用经典方法,但结果并不理想。针对神经网络和灰色系统的不足,本文基于灰色和神经网络理论,建立了灰色神经网络模型。将GM(1,4)对水库径流影响因素的数据作为神经网络的输入,将水库径流的原始数据作为神经网络的输出,对神经网络进行训练,得到最优的神经网络结构。结果表明,该模型具有较好的拟合和预测精度优势。实例分析表明,该模型对水库径流预测具有较好的准确性,具有一定的工程参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Reservior Runoff Using RBF Neural Network-Grey System United Model
At present, classic methods are used to predict reservoir runoff, but the result is not ideal. Due to the shortages of neural network and grey system, in this paper, a grey neural network model is set up based on grey and neural network theory. The data got from the GM (1,4) on the factors affecting the reservoir runoff is used as the input of the neural network and the origin data of reservoir runoff are used as the output of neural network which was trained to get the optimal structure of neural network. The results show that the model had highly fitting and predicting precision advantages than other model had. The case study shows that the model is quite accurate in prediction reservoir runoff, which has some project referential value.
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