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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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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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Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

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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