From: "Greg Heath" <>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Training multiple data for a single feedforwardnet
Date: Sat, 17 Nov 2012 17:14:14 +0000 (UTC)
Organization: The MathWorks, Inc.
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"Carlos Aragon" wrote in message <k87u2k$ng9$>...
> Greg,
> Next i present you snippets of text from a paper named " Neural Approach for Automatic Identification of Induction Motor Load Torque " from the author Alessandro Goedtel. All i'm doing is trying to redo the procedure. I think you can help me build the process according to it.
> Remember: Inputs: Voltage , Current and Speed
>                 Output: Torque
>                 Training Curves (extracted from simulink): 13
>                 Testing Curves for validation: 13
> Note: He used 100 pairs of input-output .. i'm using 5000 pairs.
> >>(...)Each curve set, simulating a specific load from 5% to 250% of nominal torque, is >>composed of a vector constituted of 100 input-output pairs, which represent the >>transient and steady behavior of the motor for one specific load and a voltage range.(...)
> >>(...) During the training process, the input-output pairs representing the process behavior >>are sequentially presented to the network(...)
> What can i understand from"sequentially present" the pairs input-output? Could you give an example on how to do it?
> >>(...)For the case of a quadratic load and a specific voltage
> >>range (198V,...,242V) 26 simulations (curves) were generated, which simulate the
> >>motor's behavior from 5% to 250% of nominal torque, where 13 were used for the >>training process and 13 for the testing process. Each training set, composed of 13 >>curves, is constituted by 100 input-output pairs, which are sequentially grouped to >>produce the training matrix. Each voltage range and each load type has a specific >>neural network.(...)
> >>After the training process, the network is able to estimate
> >>load torque curve from sequential values of speed, current and
> >>voltage. In this case, the testing process used to validate the
> >>proposed approach consists of using other operating
> >>configurations that were absent during the training process.(...)
> Ok. Once i have done for one type of load, i can do for all the other types.
> The problem: i'm still wondering how he built the training process for those 13 curves and even how he could validate for other 13 curves. All the tips given from above is that he used "other operating configurations that were absent during the training process".
> What kind of "configuration" could be it? (Show me an example, please)
> I'm concerned on how to build all the training and testing process into the neural net. Hope it's more clear for you to understand what i'm trying to do. If you can show me examples for your exaplanation it would be easier for me to understand.
> And sorry for my bad english anyway...

Why haven't you contacted the authors of the paper?