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I want to model production machines' failure in terms of MTBF(mean time between failure) and MTTR(mean time to repair), with exponential distribution.
Actually i am modelling a Discrete Event Simulatin (DEV/DEVS) problem. The problem is concerned with FMS(flexible manufacturing system). It involves six unreliable (likely to fail) machines which can fail during operation.
I want to know how to model unreliable machines in terms of MTBF & MTTR using exponential distribution.
Also, I want to study the effect of varying MTBF and MTTR values on the problem. e.g. Distribution used: Exponential;MTBF:400,600.....;MTTR:25,50...
Check this and the related folders, for examples of Stochastic Processes simulated under matlab, in particular, an example of a Poisson Process:
% poisson.m simulates a homogeneous Poisson process
lambda=10; % arrival rate
Tmax=3; % maximum time
T(1)=random('Exponential',1/lambda);
i=1;
while T(i) < Tmax,
T(i+1)=T(i)+random('Exponential',1/lambda);
i=i+1;
end
Y=zeros(1,i-1);
plot(T(1:(i-1)),Y,'.');
Just remember that, the Poisson Process is a chain of exponential events, as the very code suggests, Thus, for representing your machine, you just need to adapt the above code adding as many variables as machines you have...........
The rest, is part of your homework......
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Given the derivative of the cost function with respect to the weights or biases of the neurons of a neural network, how do I adjust these neurons to minimize the cost function?
Do I just subtract the derivative multiplied by a constant off of the individual weight and bias? If constants are involved how do I know what is reasonable to pick?
Your right about how to perform the update. This is what is done in gradient descent in its various forms. Learning rates (the constant you are referring to) are generally very small 1e-6 - 1e-8. There are numerous articles on the web covering both of these concepts.
In the interest of a direct answer though, it is good to start out with a small learning rate (on the order suggested above), and check that the loss is decreasing (via plotting). If the loss decreases, you can raise the learning rate a bit. I recommend to raise it by 3x its current value. For example, if it is 1e-6, raise it to 3e-6 and check again that your loss is still decreasing. Keep doing this until the loss is no longer decreasing nicely. This image should give some nice intuition on how learning rates affect the loss curve (image comes from Stanford's cs231n lecture series)
You want to raise the learning rate so that the model doesn't take as long to train. You don't want to raise the learning rate too much because then it is possible to overshoot the local minimum you're descending towards and for the loss to increase (the yellow curve above). This is an oversimplification because the loss landscape of a neural network is very non-convex, but this is the general intuition.
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Hy, I am working on project that based on sentiment analysis for product rating.
I have data set for good words and Negative words. When any user comment on website for product it will rate automatically out of 10
So i am confused with clustering technique and ago that solve my problem Plzzx Help
Thanks in Advance.
You are basically asking us what would be best for you to use as a classifier for your program while we have to idea how is your data stored.
However, it seems you only have two classes, positive and negative. And you want to classify new data based on word analysis of the data.
I have worked earlier in such problem, I used Rocchio's TF-IDF algorithm for such classification. You give it a set of training data (negative and positive words) and it classifies what later comes to the system.
It is based on vector classification and cosine similarity distance measure.
For more information you can read this paper.
You can find an example of how the method works (on very small data) here.
Note: the provided example is a section of a project I worked on.
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I have a large set of data to process [40000x50] values.
I use Matlab on my laptop but it takes a very long time.
Recently I had an access to an HPC station with theoretically I can process parallel computing. So how can I do that? I think I can't use Matlab without a proper toolbox for "Cloud computing" so I tried Scilab and octave but things were very complicated to me.
My main objectives are:
- Processing the data and Optimizing a model.
so my questions are:
- Do I have to work on Linux to perform parallel computing? (I use windows)
- How to perform parallel computing using a free software like Scilab or Octave ( I am a little bit familiar with Scilab).
Best regards.
Scilab provides several ways for parallel computing. A good starting point is the parallel computing wiki..
A simple example using parallel_run:
function [r_min, r_med, r_max]=min_med_max(a, b, c)
r_min=min(a,b,c); r_med=median([a,b,c]); r_max=max(a,b,c);
endfunction
N=10;
A=rand(1:N);B=rand(1:N);C=rand(1:N);
[Min,Med,Max]=parallel_run(A,B,C,"min_med_max");
Note however that this does not multi process on Windows.
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Can anybody think of a real(ish) world example of a problem that can be solved by a single neuron neural network? I'm trying to think of a trivial example to help introduce the concepts.
Using a single neuron to classification is basically logistic regression, as Gordon pointed out.
Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more metric (interval or ratio scale) independent variables. (statisticssolutions)
This is a good case to apply logistic regression:
Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent. (ats)
For a single neuron network, I find solving logic functions a good example. Assuming say a sigmoid neuron, you can demonstrate how the network solves AND and OR functions, which are linearly sepparable and how it fails to solve the XOR function which is not.
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I have a CSV data set consisting of a movie details per line.
These are: name, budget, revenue, popularity, runtime, rating, votes, date released.
I'm wondering how to split the data set into training, validation and testing sets?
Then of course, how to get some results?
It would be nice to get a brief step-by-step intro on where/how I should begin.
You should use the nntool. In your case I guess curve fitting is appropriate. So use the nftool
Define your input and output in nftool then you can just randomly divide your data into training, validation and testing sets using the nftool. In the Nftool GUI you can choose how much to divide your data (80-10-10 or anything). Then you just follow the interface and then set the specifics of the network (e.g. the number of hidden neurons). Then you just train the network. After training you can plot the performance of the training and depending on the performance you can retrain or change the number of hidden neurons, percentage of the training data and so on.
You can also check this :
http://www.mathworks.com/help/toolbox/nnet/gs/f9-35958.html