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Closed 10 years ago.
I have a vector of values corresponding to the measured data and I want to predict the next values. How can I do that? I know that is possible with Kalman filter but it might be an easier way to do. Here is a plot of the data and I want to predict next values:
Try exponential smoothing, e.g., double exponential smoothing or Holt-Winters method. Basically you try to learn the trend of the data.
I have some sample python code in this post.
On the other hand, if you know the movement/observation model of the underline variable, for sure, kalman will give you much better predictions as #tomasz74 pointed out.
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Closed 9 years ago.
I'm using the convex optimisation toolbox in matlab and the objective function gets minimized and the value is shown. But the optimisation variable for which minima is achieved, I cannot find. Will someone tell me how to find it?
You didn't say which function you are using to perform the optimization, but most or all of them return the location of the minimum as the first argument. See fmincon for example.
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Closed 9 years ago.
I'm trying to write a matlab program which is able to a random walk, but each step/vector has the same length and the thing that determines the direction is a "random" angle. The angle is not quite random since it has some specific boundary conditions. I'm fairly new to matlab, so if anybody have tips or links to webpages feel free to post them here.
Use the function rand(1) to generate an observation of a uniform random variable drawn from the range [0,1]. You can then convert this variable into the range of your angles. The code below might do the trick.
randVal = rand(1); % Generate observation of random variable
randAngle = randVal*(maxAngle-minAngle) + minAngle; % Map observation to angle
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Closed 10 years ago.
Why is Gaussian smoothing commonly used with edge detection?
What is the most suitable smoothing method for an edge detection algorithm? Is it Gaussian smoothing? If so, why?
A simple google with your own question header would've answered your question.
Basically to avoid noise affecting detection.
"Because these kernels are approximating a second derivative measurement on the image, they are very sensitive to noise. To counter this, the image is often Gaussian smoothed before applying the Laplacian filter."
from .. http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm
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Is it possible to create an ANN, which takes some data in the output just increments some counter? For example, every pass would increase the number given by a binary representation using 4 output neurons. I remember reading that the output should be same given the same input. So is that possible?
I don't think this is actually possible if one sticks to the original notion of a neural network, which is a composition of functions, each taking a weighted sum of the outputs of the previous layer as an input and producing a singe output value. The weights are usually trained to reproduce the known outputs on certain input values. The backpropagation algorithm I believe you are referring to updates the weights in a certain fashion, and the idea of making one of the weights work as a counter doesn't really make any sense.
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Closed 10 years ago.
Could you help me to write matlab simple code for k-means algorithm without using the algorithm in matlab toolbox ? So i want to work with array and plot the clusters with unique colors. For example i have an array=[1 2; 3 4 ; 5 6] with 2 clusters; some points will be red some points are blue at the end the program should plot the array in axis. then using the k-means algorithm. at the end. plot clusters in graphical interface.
can you help me?
If you want to see how MATLAB does it, type
edit kmeans
into the command window. This might give you some hints.
An easier place to start would probably be the wikipedia page, which has the basic algorithm succinctly outlined.