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i have this! RLC simple circuit
i want to analysis this circuit with different values of Resistance
but i dont know how can i draw a block diagram and simulink it with matlab, i just searched internet and found some libraries,but i didn't find anything special for my work, please if you have experience in this analysis help me and let me know how can i analysis it with MATLAB simulink
i think its very simple circuit , and if i found a way to create this in MATLAB , Matlab can analysis that easily, and i want to analysis this for different values of resistance , and i have constant L and C , for inductor and capacitor
If you're allowed and have access to it, the easiest way would be to use Simscape, which has an electrical library. See Simulink Simscape simple circuit not working for a similar question.
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This question is not about how to use matlab, but trying to find out what is happening when matab is solving a differential equation. Results are often different when using different numerical methods. i wonder which numerical method is used in matlab.
Matlab has all kinds of numerical solvers available. The basic set can be found at the bottom of this page:
https://www.mathworks.com/help/matlab/math/choose-an-ode-solver.html
If you'd like to know about a particular solver (say ode45) you can scroll to the bottom of the documentation for the given solver (for ode45 it's found here: https://www.mathworks.com/help/matlab/ref/ode45.html). For this solver the paper which explains it is linked. It may, however, be a little obtuse if you are unfamiliar with the general idea behind numerical solvers, so you might consider checking out a more pedagogical text in this area, such as the one at http://numerical.recipes/. You may also consider googling less complicated solvers like the Euler Method or the Runge-Kutta method -- both give you the flavor for how the numerical solvers work.
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Im still not sure how clustering can be used for predictive analytics?
can someone tell me how to predict the future from extracting clusters?
generally, clustering isn't used for prediction but for labeling or analyzing existing set of data points.
after you use clusters to label your data points and divide them into groups based on common traits, you can run other prediction algorithms on that labeled data to get predictions.
I don't think clustering leads directly to predictions, other than cases of clusters that are well separated and can be used to make inferences about the data points and the properties of the clusters
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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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I need to design a neural network which has the following behavior:
p(1)={0,1,1,1} outputs a(1)={0,1,0,0}
p(2)={1,1,0,1} outputs a(2)={0,0,1,0}
p(3)={0,0,1,0} outputs a(3)={0,0,0,1}
p(4)={0,0,1,1} outputs a(4)={1,1,0,1}
How can i do so? Which type of neural network should I use? Which learning method can be used here?
Thanks.
At first glance it seems as though you could use a simple feedforward neural network with one input layer one, one hidden layer and one output layer. You can use your training data to train the neural network using the backpropogation algorithm.
See this page for more details:
http://en.wikipedia.org/wiki/Backpropagation