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<>学习目标:
利用MATLAB神经网络GUI进行预测分析,得到预测结果
<>学习内容:
1、 训练数据的导入
2、 神经网络的构造
3、 神经网络的训练
4、 预测结果的得出
<>学习产出:
* 训练数据的导入
数据input04.xls中有248行5列的数据(分别是降雨量,温度,风速,风向和地下水径流量),input05.xls中有248行1列的数据(入库流量),五列数据都进行了归一化处理。构建的网络神经是希望通过四个属性特征(降雨量,温度,风速,风向和地下水径流量)来预测入库流量。
由于是将数据保存在excel文件中,我们首先要将其保存为mat格式的文件,然后再可以进行构造神经网络。
2.神经网络的构造
* 打开神经网络GUI 通过"nnstart"打开GUI工具箱,点击"Fitting app"
* 导入数据 此次数据选择的是“Matrix rows”
* 划分好训练集、测试集之后,神经元数量的选择 默认是10个隐藏神经元
3.神经网络的训练
* 训练的算法(默认算法是Levenberg-Marquardt),点击train,则开始练习
* 查看结果 其中检查R方(拟合优度)效果比较好,点击Performance可以看到它的训练过程,迭代到30步时达到最优效果。
* 保存MATAB Matrix-Only Function,保存之后便可以预测入库流量。
4. 预测结果的得出
*
比如输入降雨量(0.0453),温度(0.222),风速(0.1223),风向(0.98)和地下水径流量(0.234),便可以通过GUI给我们的函数(myNeuralNetworkFunction())来预测,预测的入库流量为0.6379。
如有问题,敬请斧正