When change the Multi-dimentional input, how to show an offset on the output in neural network?

Hi,
I am making a neural network for solar panel. I have two input: solar irradiance([200,400,600,800,1000]) and voltage(from 0 to 5V, 32 average values like a step input). The output is the Current(also 32 values.which follow the input voltage, can plot an I-V curve with the input voltage).
I have made a self-definition nueral network with two inputs, one is irradiance (200, only one value not a 5 value vector), the other is the voltage as described above. And I can get the 32-values vector current output. But I want to make a network when I change the irradiance, for example, I change the irradiance to 400, the current will also varied with it. The current will show an offset between different input irradiance. The target is I train the network with this 5 irradiance level, when I want to simulate the model, I give an input such as 900, it will give out an output curve between 800 and 1000. In the whole process, the other input "Voltage" is fixed to the 32-values vector described above.
How can I make such a model or do you have some other ideas for this problem?I am blocked by this problem for two weeks, really appreciate for your help!
Thank you !!!
Sindy

 Accepted Answer

Pair each of your 32 voltage values with a corresponding irradiance value to get an input matrix of size [2 32]. The corresponding target matrix of size [1 32] contains the resulting current values.
Use fitnet to represent a function which should interpolate between the mins and maxes of the input variables.
Hope this helps.
Thank you for formally accepting my answer.
Greg

3 Comments

Hi,Greg,
You means I can make a network on Neural network fitting toolbox? And I can use different group of input data ([2 32]) to train a function? Sorry I am new in this field and dont so clear with your meaning by "fitnet".
Thank you for answering!
Sindy
Thank you I think I got it. fitnet(hiddenSizes,trainFcn). Thank you very much!

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on 28 Jan 2013

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