"Carlos Aragon" wrote in message <k87u2k$ng9$1@newscl01ah.mathworks.com>...
> 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.
I don't know what that means. You have 3 input variables and 1 output variable.
An input-output pair consists of a 3-D input and the corresponding 1-D output?
100 such pairs make up a curve? But what constitutes "a" curve when you have 3 input variables? Are two inputs held constant and the curve consists of the output vs the one input that is varying? Or do you have 4 time series?...
> >>(...)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.(...)
Not clear.
> >>(...) During the training process, the input-output pairs representing the process behavior >>are sequentially presented to the network(...)
Is this a time series?
> What can i understand from"sequentially present" the pairs input-output? Could you give an example on how to do it?
I don't know. It is probably a time series. The only other possibility is sequential rather than batch learning.
> >>(...)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.(...)
So there are 13 networks? One for each "curve"? Again, what is plotted vs what for each curve?
> >>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)
My guess he had data for 26 curves, ordered them somehow and used every other one for
training.
> 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.
I don't know. It looks like he designed 13 nets. Maybe inputs are presented to all 13 and the target that is closest to the output identifies the correspoding load condition.
That is my best guess.
Greg![]()
> 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.
I don't know what that means. You have 3 input variables and 1 output variable.
An input-output pair consists of a 3-D input and the corresponding 1-D output?
100 such pairs make up a curve? But what constitutes "a" curve when you have 3 input variables? Are two inputs held constant and the curve consists of the output vs the one input that is varying? Or do you have 4 time series?...
> >>(...)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.(...)
Not clear.
> >>(...) During the training process, the input-output pairs representing the process behavior >>are sequentially presented to the network(...)
Is this a time series?
> What can i understand from"sequentially present" the pairs input-output? Could you give an example on how to do it?
I don't know. It is probably a time series. The only other possibility is sequential rather than batch learning.
> >>(...)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.(...)
So there are 13 networks? One for each "curve"? Again, what is plotted vs what for each curve?
> >>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)
My guess he had data for 26 curves, ordered them somehow and used every other one for
training.
> 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.
I don't know. It looks like he designed 13 nets. Maybe inputs are presented to all 13 and the target that is closest to the output identifies the correspoding load condition.
That is my best guess.
Greg