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I'm performing fault analysis on induction motor considering sag and swell. We have a set of data for all types of sag and swell and our analysis includes healthy/balanced supply.
What are the inputs, samples and targets to be considered in building neural networks and how to train the network in MATLAB.
If your matlab installed neural network toolbox, here are plenty of examples that you can learn from.
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I would like to use the Autonomous Driving Toolbox in Matlab. But I prefer to know more abou it before I buy it. For instance, is it possible to customize some modules in the toolbox with some different algorithms than the default? For example, in the motion planning section of the toolbox, there's only one planner module provided by Matlab called "pathPlannerRRT" which implements the RRT algorithm for path planning, is it possible to create another customized planner with another or self-designed algorithm?
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I am studying J. Schesser's work about HRV in ECG signals.
I am waiting for his answer to the case but also others' opinions and reviews. He has used Labview in most of his ECG data analysis.
I am thinking why, since much of my known electronics do not limit me there. I can use Matlab too.
I want to study time and frequency domains in HRV analysis of ECG signals.
I am mostly using Analog.com's electronics in my ECG boards and systems so they should not be limited to proprietary frontends like Labview.
Why Labview is used in some analysis parts of HRV?
I think it is mostly for NI's LabVIEW Biomedical Toolkit
http://sine.ni.com/nips/cds/view/p/lang/en/nid/211023
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I would like to perform system identification on a MIMO (multiple input, multiple output) system in Matlab. Are there any functions or toolboxes available to do this?
Is it possible to perform system identification on a MIMO (multiple input, multiple output) system in Matlab?
Yes.
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What is the best way to implement group algebra using Matlab. More specifically, I only want to work with permutation groups (and their subgroups) but would like a way to find kernels and cosets without coding a brute force version myself.
GAP is a 'System for Computational Discrete Algebra' and is able to do all the things requested in the question.
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I'm learning about multilayer perceptrons, and looking to tackle some real-world problems in Matlab. Perhaps something like medical diagnosis, or speech recognition... But I'm not really sure where to start here. How should I tackle this?
Geoff Hinton is the man when it comes to multilayer perceptrons. His Science paper from 2006 used a special class of MLP called an "autoencoder" that was successful in digit recognition, facial recognition, and document classification (all of which have real world applications):
Reducing the Dimensionality of Data with Neural Networks
Fortunately, they also published the
Matlab code.