改进型灰色神经网络模型在水质预测中的应用

Application of improved grey neural network model to water quality prediction

介绍了三类改进的灰色模型和BP神经网络。将三类改进的灰色模型与神经网络进行组合,得到改进型灰色神经网络组合模型,将一维序列通过三个改进的灰色模型得到三组值作为神经网络的输入,原始序列作为神经网络的输出,训练得到最佳神经网络结构。将组合模型应用到嘉陵江磁器口断面总磷浓度的预测中,结果表明:(1)用该组合模型进行预测,相对误差均在5%以下,预测精度较高,取得了较理想的预测效果;(2)WPGM(1,1)、pGM(1,1)、CGM(1,1)、组合模型预测的平均相对误差分别为5.05%、34.01%、33.65%、3.02%,与单一灰色预测方法和灰色神经网络模型相比,组合模型的适应能力和预测推广能力更好,预测精度更高。

The three types of improved grey model and BP artificial neural network are introduced. Improved grey neural network combined model was combined three categories of improved grey model and neural network. Then got three sets of number which is made use of three improved grey prediction models with one-dimensional sequence as the neural network＇ s inputs, and the original sequence was used as the output of the neural network. The neural network was trained to get the optimal structure. The combined model was applied to predict the total phosphorus concentration of section of Ciqikou Jialing River. The results indicated that the first combined model obtained highly precise forecast result, with relative errors below 5%. The second average relative errors of prediction of WPGM （ 1, 1 ） ,pGM（ 1, 1 ） ,CGM（ 1, 1 ） and combined models is respective 5.05% , 34. 01% , 33.65% and 3.02% , its adaptive capacity,ability of promotion and precision were better than only a single predictive method of grey and grey neural network model.