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Topic: neural network
Replies: 2   Last Post: Jan 15, 2013 5:12 AM

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Greg Heath

Posts: 6,387
Registered: 12/7/04
Re: neural network
Posted: Jan 15, 2013 5:12 AM
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"Greg Heath" <> wrote in message <kd0hoe$p0s$>...
> "Jamaa Ambarak" <> wrote in message <kcqpbo$4dq$>...
> > First of all , I have deal with real data that used for lane predication and I have collected my data and now I want to use it to predict lane deviation of a car, I have used time series for prediction and I have selected 3 features for that and they" lateral position, steer-angle and speed velocity." the data is about 3 .5 hours of driving , I have collected data from 15 drivers and the drivers are drowses some of them they draft 200 , 360
> >times of the lane,

Do you mean

" the drivers are drowsy and some of them drift out of the lane as much as 200 to 360 times in ~ a 14 minute period"?

200 to 360 consecutive times?

> >so I have selected time windowing from lane deviation cases(out of the lane ) and the > >normal cases(in the lane) for training .the window size is 1second =50 samples

So the 200 to 360 times means 4 to ~7 consecutive seconds in ~ a 14 minute period?

> >and the 3 features are represented in matrix as "150x400" as example 50 samples lateral position, 50 samples speed velocity, and 50 samples steer-angle. The 400 are the other cases in one matrix.
> > For testing data I want to use sliding window to test whole driver. I use the sliding window for 3 features, the data is big it is about 500.000 for one driver and I want to test sample by sample for example [1 2 3 4 5 6..........150] Transpose. The second one will be > >[2 3 4 5 6.....151] Transpose. And so on ? the matrix will be as [150X405316].

Have you considered having a I-H-O net (O=1, I = 3*n, n= 1,2,...) where any combination of position, angle and velocity would correspond to a target of 0 or 1?

I would start there and then increase the input dimension by factors of 3.

If you don't use a validation stopping subset or regularization (trainbr/msereg), N input-output training pairs of dimension 3n and 1, respectively, yields

Neq = N*O = N % Number of training equations
Nw = (I+1)*H + (H+1)*O % Number of unknown weights to estimate

Then Neq > Nw or H < Hub is required where

Hub = floor( (Neq-O)/(I+O+1)) = floor((N-1)/(3*n+2))

However, it desired that H<< Hub or at least H < Hub/2

Have you considered the significant crosscorrelation lags between the inputs and output?

What about the significant crosscorrelation lags between the inputs and the significant autocorrelation lags of each input?

> It is hard for me to believe that you really need 50 samples a second to determine lane deviation. How much subsampling have you investigated?


Date Subject Author
Read Re: neural network
Greg Heath
Read Re: neural network
Greg Heath

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