NARXNET with input delay

Question

Ghazi Binarandi 2016-2-13

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评论： Ghazi Binarandi 2016-2-18

Hi,

I am solving a problem incorporating measurement delay in a sensor. I have a set of measured acceleration based on the sensor reading (in time series). However, the measurements that I obtained have some time delay from the 'true' value of it. I want to design a neural network that can 'predict' the true acceleration value using the measured value that I obtained from the sensor measurement.

I am using NARXNET to model the neural network of it. To accommodate the delay in the input, 'inputDelays' is adjusted according to the delay in the sensor measurement (and, technically, no delay is needed for the 'feedbackDelays'). Am I doing it correctly?

Thank you,

Ghazi

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Answer 1

Greg Heath 2016-2-14

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在 MATLAB Online 中打开

No.

TIMEDELAYNET is used to predict an output series, y, using an exogeneous external input series x.

NARXNET is used to predict an output series, y, using BOTH an exogeneous external input series x, along with feedback signals from the previous estimated values of y.

HOWEVER, you do not have an exogeneous external input series!

Therefore, you should use NARNET. First read the documentation

 help narnet
 doc narnet

Then search for some of my posts in BOTH the NEWSGROUP AND ANSWERS using

narnet greg

and

narnet greg tutorial

Begin by finding the statistically significant nonzero lags at the highest local peaks of the measurement autocorrelation function. Search

nncorr narnet

Then use as small a continuous subset of lags as needed for your predictions.

Hope this helps.

Thank you for formally accepting my answer

Greg

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Greg Heath 2016-2-17

How do you obtain the true values you use for design targets?

Ghazi Binarandi 2016-2-18

I have another type of sensor which has better performance in terms of delay. So, I could model the measurements from this sensor as the true/ideal values (these are going to be my target). Then, by training it with neural nets, I could use the sensors with delay on it, but still obtain a performance that is close to the better sensor.

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