Why do i obtain good results but poor input error cross correlation plot?

Question

WT 2014-11-6

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/161531-why-do-i-obtain-good-results-but-poor-input-error-cross-correlation-plot

评论： Greg Heath 2014-11-7

Hi,

May i know why do i obtain a low MSE and good predicted outputs but poor input error cross correlation plot?The correlations do not fall within the confidence limit. I am using neural networks time series tool. Why does the confidence limit varies for different MSE results? Currently mine my confidence limit is at +- 2000. Other MSE results can give me confidence limit of +- 4000 but with higher MSE. May i know the reason for this?

Thank you

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Greg Heath 2014-11-6

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/161531-why-do-i-obtain-good-results-but-poor-input-error-cross-correlation-plot#answer_157906

If the input/error cross-correlation is large, then a different set of weights could use that correlation to further improve performance.

The best result would be an error that is not at all correlated with either input or output.

Hope this helps.

Thank you for formally accepting my answer

Greg

3 个评论
显示 1更早的评论隐藏 1更早的评论

Greg Heath 2014-11-7

在 MATLAB Online 中打开

How are you obtaining the MSE confidence limit?

Why not use normalized MSE values (divide by average target variance, MSE00) to remove the obscuration of target scaling?

Different weights associated with a different design.

 a. divideblock instead of dividerand 
 b. Better choice of delays (See my correlation function method of choosing delays). Search NEWSGROUP and ANSWERS using
 greg ncorr
 c. Better combination of hidden nodes and random initial weights See my double for loop method of optimizing number of hidden nodes and initial weights):
 greg narxnet Ntrials

Hope this helps.

Thank you for formally accepting my answer

Greg

Greg Heath 2014-11-7

To be clear:

Training results are biased because the data used to obtain performance summaries are the same data used to design the model. However, if the number of training equations is larger than the number of unknown weights, a degree of freedom correction is useful.

Validation results are also biased because the data is used to determine when to stop the design. However, the bias is much smaller. Therefore, the val performance should be used to choose among competing designs.

Therefore, test performance which, in no way is involved in design, is the only UNBIASED prediction of performance on unseen data.

Hope this helps.

Greg

请先登录，再进行评论。