How shall I activate signalA output signal when input signal value is 0 to 2.
Example: when input signal is 2, active output.
When input signal is other than 2, reactive output.
Which means, when input signal value is from decending 5 to 2,output signal shall not active. Only need to active ascending 0 to 2 using simulink blocks
Related
How can I generate a simulink model to tell me if a certain output x signal has reached a threshold level after n seconds during the system run time? I would like to consider the last value of x and if so, enable an alarm value of -1.
I assume you want to do the following comparison:
x(n) >= threshold
In Simulink there is a block called "weighted sample time" that you can use to know the sample time. Then you can use a comparator to check if sample time is equal to n and another comparator to make sure x is greater than n
I am running the model in external mode in real time hardware with discrete fixed step solver. I need to generate an input signal to my hardware as boolean 0 and 1 with sample time 0.01.
1.Is there any block to generate 0 and 1 as input signal in simulink.
2.Also another doubt, with input start for 2second as 1 and 5 second as 0 and another desired (as user desired time it can give input as 1 or 0 )
Kindly suggest me.
Hi my question is a bit long please bare and read it till the end.
I am working on a project with 30 participants. We have two type of data set (first data set has 30 rows and 160 columns , and second data set has the same 30 rows and 200 columns as outputs=y and these outputs are independent), what i want to do is to use the first data set and predict the second data set outputs.As first data set was rectangular type and had high dimension i have used factor analysis and now have 19 factors that cover up to 98% of the variance. Now i want to use these 19 factors for predicting the outputs of the second data set.
I am using neuralnet and backpropogation and everything goes well and my results are really close to outputs.
My questions :
1- as my inputs are the factors ( they are between -1 and 1 ) and my outputs scale are between 4 to 10000 and integer , should i still scaled them before running neural network ?
2-I scaled the data ( both input and outputs ) and then predicted with neuralnet , then i check the MSE error it was so high like 6000 while my prediction and real output are so close to each other. But if i rescale the prediction and outputs then check The MSE its near zero. Is it unbiased to rescale and then check the MSE ?
3- I read that it is better to not scale the output from the beginning but if i just scale the inputs all my prediction are 1. Is it correct to not to scale the outputs ?
4- If i want to plot the ROC curve how can i do it. Because my results are never equal to real outputs ?
Thank you for reading my question
[edit#1]: There is a publication on how to produce ROC curves using neural network results
http://www.lcc.uma.es/~jja/recidiva/048.pdf
1) You can scale your values (using minmax, for example). But only scale your training data set. Save the parameters used in the scaling process (in minmax they would be the min and max values by which the data is scaled). Only then, you can scale your test data set WITH the min and max values you got from the training data set. Remember, with the test data set you are trying to mimic the process of classifying unseen data. Unseen data is scaled with your scaling parameters from the testing data set.
2) When talking about errors, do mention which data set the error was computed on. You can compute an error function (in fact, there are different error functions, one of them, the mean squared error, or MSE) on the training data set, and one for your test data set.
4) Think about this: Let's say you train a network with the testing data set,and it only has 1 neuron in the output layer . Then, you present it with the test data set. Depending on which transfer function (activation function) you use in the output layer, you will get a value for each exemplar. Let's assume you use a sigmoid transfer function, where the max and min values are 1 and 0. That means the predictions will be limited to values between 1 and 0.
Let's also say that your target labels ("truth") only contains discrete values of 0 and 1 (indicating which class the exemplar belongs to).
targetLabels=[0 1 0 0 0 1 0 ];
NNprediction=[0.2 0.8 0.1 0.3 0.4 0.7 0.2];
How do you interpret this?
You can apply a hard-limiting function such that the NNprediction vector only contains the discreet values 0 and 1. Let's say you use a threshold of 0.5:
NNprediction_thresh_0.5 = [0 1 0 0 0 1 0];
vs.
targetLabels =[0 1 0 0 0 1 0];
With this information you can compute your False Positives, FN, TP, and TN (and a bunch of additional derived metrics such as True Positive Rate = TP/(TP+FN) ).
If you had a ROC curve showing the False Negative Rate vs. True Positive Rate, this would be a single point in the plot. However, if you vary the threshold in the hard-limit function, you can get all the values you need for a complete curve.
Makes sense? See the dependencies of one process on the others?
Im learning(started today) neural networks and could finish a 2x2x1 network(forward data feeding and backward error propagated) that can learn AND operation for one set of inputs. It also dodges any local minimums using randomized parameters. My first source for this is: http://www.codeproject.com/Articles/14342/Designing-And-Implementing-A-Neural-Network-Librar
The problem is: it learns 0 AND 0 using inputs (0,0) but when I give (0,1) it forgets 0 AND 0 then learns 0 AND 1. Is this a general newbie bug?
What I tried:
loop for 10000 times
learn 0 and 0
end loop
loop for 10000 times
learn 0 and 1 (forgets 0 and 0)
end loop
loop for 10000 times
learn 1 and 0 (forgets 0 and 1)
end loop
loop for 10000 times
learn 1 and 1 (forgets 1 and 0)
end loop
only one set is learned
fail
Trial 2:
loop for 10000 times
learn 0 and 0
learn 0 and 1
learn 1 and 0
learn 1 and 1
end loop
gives same result for all input combinations.
fail.
Activation function for each neuron: hyperbolic tangent
2x2 structure: all-pairs
2x1 structure: all-pairs
Randomized learning rate: yes, small enough to keep far from explosive iteration (per iteration)
Randomized bias per neuron: yes, between -0.5 and +0.5 (just at start)
Randomized weighting: yes, between -0.5 and +0.5 (just at start)
Edit: Bias and weight updates are done for all-pairs of hidden and output layers.
Edit: All neurons(hidden+output) use same activation function.
Without specific code it is hard to say for sure, but I think the issue is that you are only giving it one case to learn at a time. You should give it a matrix of your different learning examples, with an expected result vector. Then, when you update your weights and biases, you are finding the values that minimize the error between your network output for all cases, and the expected output for all cases.
For an AND gate, your input would be (in MATLAB code, not sure what language you are using but that syntax is easy to understand):
input = [0, 0;
0, 1;
1, 0;
1, 1];
And your expected output would be:
output = [0;
0;
0;
1];
I think what you are doing now is basically finding the weights and biases that minimize the error between the network output and the expected output for just one input case, and then re-training those weights and biases to minimize the error for the second case, then the third, then the fourth. If you put them in arrays like this it should minimize the overall error for all cases. This is just my best guess though without any code to go on.
I made a neural network that also have Back Propagation.it has 5 nodes in input layer,6 nodes in hidden layer,1 node in output layer and have random weights and i use sigmoid as activation function.
i have two set of data for input.
for example :
13.5 22.27 0 0 0 desired value=0.02
7 19 4 7 2 desired value=0.03
now i train the network with 5000 iteration or iteration will stop if the error
value(desired - calculated output value) is less than or equal to 0.001.
the output value of first iteration for each input set is about 60 And it will decrease in each iteration.
now the problem is that the second set of inputs(that has desired value of 0.03),cause to stop iteration because of calculated output value of 3.001 but the first set of inputs did not arrived to desired value of it(that is 0.02) and its output is about 0.03 .
EDITED :
I used LMS algorithm andchanged the error threshold 0.00001 to find correct error value,but now output value of last iteration for both 0.03 and 0.02 desired value is between 0.023 and 0.027 and that is incorrect yet.
For your error value stop threshold, you should take the error on one epoch (Sum of every error of all your dataset) and not only on one member of you dataset. With this you will have to increase the value of your error threshold but it will force your neural network to do a good classification on all your example and not only on some example.