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[prashantgujjar]

Differrence between feed forward & feed forward back propagation

I used neural netowrk MLP type to pridect solar irradiance, in my code i used fitnet() commands (feed forward)to creat a neural network.But some people use a newff() commands (feed forward back propagation) to creat their neural network. please what's difference between two types?? :

net=fitnet(Nubmer of nodes in haidden layer); --> it's a feed forward ?? true??   net=newff(Nubmer of nodes in haidden layer); ---> it's a feed forward back propagation ??

Help me please wchich one can i choose for my case (prediction problem)???!!! Who appripriate??

ANSWER

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1. Regardless of how it is trained, the signals in a feedforward network flow in one direction: from input, through successive hidden layers, to the output.

2. Given a trained feedforward network, it is IMPOSSIBLE to tell how it was trained (e.g., genetic, backpropagation or trial and error)

3. A feedforward backpropagation net is a net that just happened to be trained with a backpropagation training algorithm. The backpropagation training algorithm subtracts the training output from the target (desired answer) to obtain the error signal. It then goes BACK to adjust the weights and biases in the input and hidden layers to reduce the error.

4. Current examples of feedforward nets are

a. FITNET for regression and curve-fitting which calls the generic FEEDFORWARDNET b. PATTERNNET for classification and pattern-recognition which calls the generic FEEDFORWARDNET c. FEEDFORWARDNET

5. OBSOLETE examples of feedforward nets are

a. NEWFIT for regression and curve-fitting which calls the generic NEWFF b. NEWPR for classification and pattern-recognition which calls the generic NEWFF c. NEWFF

6. The default training functions for the above algorithms use backpropagation. However, the designer can train the nets any way they want.

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[prashantgujjar]

Do the functions FILTER2 and CONV2 perform correlation or convolution?

Do the functions FILTER2 and CONV2 perform correlation or convolution?

ANSWER

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The function FILTER2 performs correlation, while the function CONV2 performs convolution. To see what's going on, you need to compare the output of the 'full' option with the output of the 'valid' option. If you compare these two, you'll see that the 'valid' is taking the same submatrix in both cases. The full and valid matrices are actually not dependent on the values of "f".

For example:  

a = magic(5) a =     17    24     1     8    15     23     5     7    14    16      4     6    13    20    22     10    12    19    21     3     11    18    25     2     9 f = [1 0; 0 0]; filter2(f,a,'full') ans =      0     0     0     0     0     0      0    17    24     1     8    15      0    23     5     7    14    16      0     4     6    13    20    22      0    10    12    19    21     3      0    11    18    25     2     9 filter2(f,a,'valid') ans =     17    24     1     8     23     5     7    14      4     6    13    20     10    12    19    21

The 'valid' result is the (2:5,2:5) submatrix of the 'full' result.

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[prashantgujjar]

Removing noise from a sine wave

Hello, I have two sets of measurement data (sine wave of 10 = Hz and fs = 2048 Hz) with lots of noise. I tried to filter them using a butterworth lowpass filter using filtfilt function.

Unfortunately, the filtered signal is not a pure sine wave and hence i couldn't use them further.

Can anyone guide me how exactly to process this signal? 

PS: Can i add .fig to my post?

ANSWER

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You might try the Savitzky Golay filter, in the Signal Processing Toolbox. It fits a polynomial to the signal in a sliding window. 

My demo below is for an image but you could easily adapt it to a 1D signal. After all, an image is just a bunch of 1D signals stacked on top of each other (each row or column could be considered a 1D signal). That's what I do, just extract a row or column and call sgolayfilt() on each row or column.

% Filter using Savitzky-Golay filtering. % By Image Analyst % Change the current folder to the folder of this m-file. if(~isdeployed)  cd(fileparts(which(mfilename))); end clc;  % Clear command window. clear;  % Delete all variables. close all;  % Close all figure windows except those created by imtool. imtool close all;  % Close all figure windows created by imtool. workspace;  % Make sure the workspace panel is showing. fontSize = 14; % Read in standard MATLAB gray scale demo image. % imageArray = imread('football.jpg'); imageArray = imread('cameraman.tif'); imageArray = double(imageArray); [rows columns numberOfColorBands] = size(imageArray); subplot(2, 2, 1);

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