[scikit-learn] Artificial neural network not learning lower values of the training sample

[muhammad waseem]

This problem has been solved in the literature before, I can post papers.

On Tue, May 31, 2016 at 7:07 PM, Jacob Schreiber <jmschreiber91 at gmail.com>
wrote:

> Do you have any other baselines which you can compare to? It might be
> helpful in seeing if this is a problem which can be learned.
>
> On Tue, May 31, 2016 at 10:47 AM, muhammad waseem <
> m.waseem.ahmad at 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 at 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 <http://twitter.com/andrewholmes82>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On 31 May 2016, at 16:55, muhammad waseem <m.waseem.ahmad at 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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>>>
>>>
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>>
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