I am using Encog Workbench Analysis Wizard.
As I understood "target field" is the output neuron. Nevertheless, in my network I have 75 input neurons and 44 output neurons. I cannot figure out how I set up more than one target field in the Analysis Wizard. Could anyone help with it, please? Or do I misunderstand the conception of the target field in this case?
Run the analyst wizard as normal. Let it choose one field for a single output. Then go into the .ega file. Scroll down to where it says [NORMALIZE:RANGE]. You will see a list of inputs and one output at the end. Now change the word input to output for all the relevant neurons.
Then you can change equilateral to oneof for all the target fields if you are using a 1 of n classification.
Yes target and output mean the same thing in this case.
If you are starting out with this, I highly recommend you begin the problem with say 5 or 10 input and output neurons, to greatly simplify your problem.
Related
I'm trying very hard to understand this code about a Deep Learning-Based NOMA system based in MATLAB. I am really new to MATLAB coding but I really need to understand this entire code as it will help in my school project and I am struggling.
I think as of right now I do not need to know how the mathematical formulas work, but instead, the focus is on what the code is doing and its flow.
This is part of the code in the trainData.m file that I am struggling with right now
Why are the pilot symbols calculated and then replaced right after?
Why is the idx_sc (20) selected to be replaced? What is its significance? Is it the only subcarrier selected for the training of the DL model? Why only that?
This portion of the code in the picture is labeled "generate training data for each class". From my understanding, it is generating OFDM packets for each label, simulating the transmission and reception, and then getting the features and labels for each of the 16 classes. Is that correct?
The code and all relevant function files can be found in the link below.
Please help me understand the code!!! Please! Much thanks!
https://www.mathworks.com/matlabcentral/fileexchange/75478-deep-learning-for-signal-detection-in-noma-systems
To get you started, In lines 91 the code initializes the entire variable as 0. Subsequent lines (92-96) are just replacing pieces of the variable based on the indexing inside the “(…)”
I am implementing a fuzzy logic based decision support system that uses nine variables but group each three together to form an output then take these three output to make the final output of the system.
I am using fuzzy logic toolbox in matlab, I made each one of these three outputs but I can't figure out how I can make these outputs as inputs again for the final output.
The system is shown in this picture:
system picture
Correct me if I'm wrong, but I think you would need two FIS separately , where the inputs from the second one will be the outputs from the first one.
Sorry If I didn't help much :/
New with Matlab.
When I try to load my own date using the NN pattern recognition app window, I can load the source data, but not the target (it is never on the drop down list). Both source and target are in the same directory. Source is 5000 observations with 400 vars per observation and target can take on 10 different values (recognizing digits). Any Ideas?
Before you do anything with your own data you might want to try out the example data sets available in the toolbox. That should make many problems easier to find later on because they definitely work, so you can see what's wrong with your code.
Regarding your actual question: Without more details, e.g. what your matrices contain and what their dimensions are, it's hard to help you. In your case some of the problems mentioned here might be similar to yours:
http://www.mathworks.com/matlabcentral/answers/17531-problem-with-targets-in-nprtool
From what I understand about nprtool your targets have to consist of a matrix with only one 1 (for the correct class) in either row or column (depending on the input matrix), so make sure that's the case.
I have been using the Encog Neural Net workbench (version 3.2) to run the sunspot prediction routine and have noticed that when changing the future prediction window to greater than 1 the results in the sunspot_output.csv appear to be time offset so that the output when the network evaluates at t=0 are not really (t+1), (t+2), (t+3) etc. It's very likely I'm not understanding how the workbench is displaying the results so perhaps someone could clarify this for me.
As I understand it if you use a past window of 30 and a future window of 14 then the network will look at the last 30 records and predict forward from the last available record (in this case lets say 11/1/1951 is the last available record). So an evaluation on 11/1/1951 will look back 30 records to 5/1/1949 and use this information to feed through the trained network to predict data for 12/1/1951 (t+1), 1/1/1952 (t+2), 2/1/1952 (t+3), etc. However, looking at the result file this does not appear to be the case. The "prediction" really appears to be a repeat of the pattern from the previous 14 records. So that (t+1) is really more representative of (t-14) 08/01/1950 than the next record forward from (t=0) which would be on 12/1/1951.
I have an image that shows this but unfortunately I don't appear have the reputation points to post it yet. To reproduce this issue I suggest using the Encog workbench and using a past window of 30, future window of 14 and training error of 1 or 2%.
To Summarize:
Has anyone else noticed this issue when looking at the predictive network results, particularly for greater than one time step ahead?
Why do the workbench results show that the encog predictive neural network is not properly predicting into the future when you look at the dates associated with the outputs.
Thank you for any thoughts you may have!
That's not an issue is how a sliding window time series forecaster works.
I would suggest you to deepen here https://www.cs.rutgers.edu/~pazzani/Publications/survey.pdf
It really depends on how you tune the neural network.
If you want more predictive power you have to extract features or syntetize new features (for example I would use wavelet extraction and denoising).
Pay attention to normalization. Use range normalization if you know that there are known ranges otherwise z-normalization.
Use the proper activation function: Sigmoid if the normalized range is 0,1 or tanh if the range is -1,1.
But before ending that the neural network is not predicting I would suggest you to use SVR (Support Vector Regression) included in encog.
It guarantees (if it is present) to reach the global minimum.
See if the SVR predicts better than the ANN.
If not use my firsts suggestions ;-)
Vincenzo
I need a help related to som tool box. Here is the question.
"we have 100 input records(which belongs to two classes) how do we know from SOMToolbox output whether a particular input say number 35 is clustered as class 1 or class 2, can we finalize it from u-matrix or is there any other file , we want to final the accuracy from the SOM output"
please help me if you know the answer and waiting for your suggestions
You can specify the no of data to be trained, or you can stop the training after your limit. One can see the changes on mesh live as the data are being processed in som-toolbox in matlab.