According to the NI-Labs main page, it requires:
LabVIEW RT 8.6
IT does not say if it is AND or OR. 😉 Does it require both or is LabVIEW 8.6 sufficient? Form what I can tell, the demo runs just fine in 8.6 (non-RT).
As a matter of fact, the example seems to be in 8.5 and everything runs fine in 8.5. Is there anything that would prevent people from trying this in 8.5?
You are absolutely correct. It can run on LabVIEW desktop or LabVIEW RT or LabVIEW Microprocessor SDK. It is written in LabVIEW 8.5, so it should be fine for any version later than that.
Thanks for pointing that out.
It's what I want to have
I think you should add a normlization SubVI. It's very important to ANN.
May I have you Email? GerMo. If I have other question, I can send a email to you.
My email: firstname.lastname@example.org
Thanks for the comments.
I am interested to hear more about "normalization functions." Do you mean to normalize inputs? The more specific your comments, the better!
The best way to reach me is by posting on this message board.
The stability of ANN is sensitivity to initializing weight matrices and input data. Yout activation function is sigmoid,so then the output < 0.1 or >0.9, the weight matrices must change a lot .It's will use many time. I think the input and output data for trainning ANN must normalize. Make the data between 0.1 to 0.9 is easy to train and use. I wrote a VI for that. But I can't upload in this BBS.
Furthermore，I'm very interesting in "40-20-40" rule, May you give me your reference paper? I don't know how you get the 0.3.
Thanks for the information about normalization.
Regarding the 40-20-40 rule, it is sometimes called "Fahlman's rule". Try this link, for example, or search for "fahlman 40-20-40 neural nets"
Is it possible to utilize ANN for such things as adaptive temperature control? I have a very non-linear application controlling a Heat Exchanger and have tried both PID and Fuzzy Logic. The PID works
well within certain operating ranges (such as inlet water temperature) but would need quite a bit of gain scheduling to work across all conditions. Fuzzy Logic is more flexible but I have it set up as a
'bias-less' controller and thus does not have very good response.