使用神经网络早期检测峰 [英] Early Detection of peaks with Neural Network

问题描述

我正在使用神经网络技术(向后学习).作为输出，例如，我给出了18点的超前值，作为输入，我给出了要训练的最新的5点(我尝试了输入数据5,10,20,30 ...的许多组合).

I am using Neural Network technique(Backward Learning). As a output for example I am giving the 18 point ahead value and as input I gave the latest 5 point to train.(I tried the many combinations of input data 5,10, 20 ,30...).

例如，我训练数据的方式:

For example, the way I trained my data:

t，t + 1，t + 2，t + 3，t + 4 ... => t + 22(4 + 18)

t, t+1, t+2, t+3, t+4... => t+22(4+18)

t + 1，t + 2，t + 3，t + 4，t + 5 ... => t + 23

t+1, t+2, t+3, t+4, t+5... => t+23

指数输入:

t，t + 1，t + 2，t + 4，t + 8 ... => t + 26(8 + 18)

t, t+1, t+2, t+4, t+8... => t+26(8+18)

t + 1，t + 2，t + 3，t + 8，t + 9 ... => t + 27

t+1, t+2, t+3, t+8, t+9... => t+27

培训后，我已经按照自己的价值观进行了进阶学习.我已经观察到神经网络无法捕捉突然出现的峰值.在大多数情况下，如果我要预测18秒，它将在17秒后预测正确的结果.

After I trained, I have done forward learning with the my trained values. I have observed that neural network won't able to catch the sudden peaks. Most of the time if I am going to predict 18 seconds ahead, it predicts the correct result 17 seconds later.

您对我如何使用神经网络预测突发峰值(几秒钟后会发生)有任何建议吗?

Do you have any advice for me about how could I able to predict sudden peaks(that will happen t seconds later) with neural network?

推荐答案

我使用反向传播，并且观察到相同的行为.如果我理解正确，则您没有真正的预测.数据中存在偷看功能时，只有在出现在系列中之后才可以对其进行预测"，并且您会观察并明显延迟预测.

I work with backpropagation and I observe the same behaviour. If I understood correctly, you don't have a true forecasting. Peeks, when present on data, are available to be "predicted" only after appear in the series, and you observe and apparent prediction with a delay.

我想您必须使用循环网络.

I thinf you have to user a recurrent network.

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