What about adding a simple binary night / day flag?  While it's less information than hour, it will provide a distinct cutoff for the network to use.

Andrew

<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD

Editor-in-Chief, European Journal of Mathematical Sciences
Executive Editor, European Journal of Pure and Applied Mathematics
www.andrewhowe.com

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On Tue, May 31, 2016 at 8:47 PM, muhammad waseem <m.waseem.ahmad@gmail.com> wrote:

Thanks for your reply. I have day, month, hour, temp, relative humidity, Wind speed as my input variables. I can't think of any other dependant variables. It is quite strange to me that I don't get results after using these input variables. 

On Tue, May 31, 2016 at 4:59 PM, Andrew Holmes <andrewholmes82@icloud.com> wrote:

If the problem is that it’s confusing day and night, are you including time of day as a parameter?

Best wishes

Andrew

@andrewholmes82

On 31 May 2016, at 16:55, muhammad waseem <m.waseem.ahmad@gmail.com> wrote:

Hi All,

I am trying to train an ANN but until now it is not learning the lower values of the training sample. I have tried using different python libraries to train ANN. The aim is to predict solar radiation from other weather parameters (regression problem). I think the ANN is confusing lower values (winter/cloudy days) with the night-time values (probably). I have tried the following but none of them worked;

 1. Scaling data between different values e.g. [0,1],[-1,1]

 2. Standardising data to have zero mean and unit variance

 3. Shuffling the data

 4. Increasing the training samples (from 3 years to 10 years)

 5. Using different train functions

 6. Trying different transfer functions

 7. Using few input variables

 8. Varying hidden layers and hidden layers' neurons

Any idea what could be wrong or any directions to try?

Thanks

Kindest Regards

Waseem

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