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Q:Difference between regression and performance plot of Artificial neural network in MATLAB

Q:在MATLAB回归分析和人工神经网络的性能曲线之间的差异

I am having problem understanding regression and performance plots of ANN. My data consists of 13 inputs and 3 outputs. Parameters used for simulation are as follows.

The problem I am facing is that I get a very good fitted regression plot as follows (Performance and regression plots).

This means that the model is well fitted, but on the other hand the mean squared error is quite high. Which is strange thing for me. If the regression plot is well fitted then the MSE should be small, or I am wrong?

我有问题的理解回归和性能图的人工神经网络。我的数据由13个输入和3个输出组成。用于仿真的参数如下。

我所面临的问题是,我得到一个很好的拟合回归图如下(性能和回归图)。

这意味着,该模型拟合良好,但在另一方面的均方误差是相当高的。这对我来说是件奇怪的事。如果回归情节符合然后MSE要小,或者是我错了吗?

answer1: 回答1:

The mean squared error has to be seen in relation to the size of the values you are fitting. It seems your values are on the order of 10^7, and your MSE is on the order of 10^10. This means, after taking the square root, your errors have size on the order of 10^5, which is about 1% error when viewed in relation to the magnitude of your target values.

平均平方误差必须与你所拟合的值的大小有关。看来你的价值观是对10 ^为7,你在10 ^均为10。这意味着,在采取平方根,您的错误有大小为10的顺序5,这是约1%的错误时查看有关您的目标值的大小。

performance  matlab  machine-learning  neural-network  regression