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4. Selected Applications 147

Table 4-1. Sample Hetero Association table relating input vectors and output vectors
of the BPNN

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 Y
X(10) X(9) X(8) X(7) X(6) X(5) X(4) X(3) X(2) X(1) X(0) Y(0)
X(11) X(10) X(9) X(8) X(7) X(6) X(5) X(4) X(3) X(2) X(1) Y(1)
X(12) X(11) X(10) X(9) X(8) X(7) X(6) X(5) X(4) X(3) X(2) Y(2)
…
X(16) X(15) X(14) X(13) X(12) X(11) X(10) X(9) X(8) X(7) X(6) Y(16)
X(17) X(16) X(15) X(14) X(13) X(12) X(11) X(10) X(9) X(8) X(7) Y(17)
X(18) X(17) X(16) X(15) X(14) X(13) X(12) X(11) X(10) X(9) X(8) Y(18)
X(19) X(18) X(17) X(16) X(15) X(14) X(13) X(12) X(11) X(10) X(9) Y(19)
X(20) X(19) X(18) X(17) X(16) X(15) X(14) X(13) X(12) X(11) X(10) Y(20)
…

The Hetero association table relating the sample input vector and the
corresponding output vector used for training the constructed ANN is given
below. Note that x is the corrupted reference signal and y is the reference
signal

Step 4: Train the Artificial Backpropagation Neural Network as described
in the chapter 1. Store the Weights and Bias.

Step 5: Now the BPNN filter is ready to filter the original corrupted signal.

3.2 M-file for Noise Filtering Using ANN

_______________________________________________________

noisefiltuanngv.m

%Noise filtering using ANN
A=wavread('C:\Program Files\NetMeeting\Testsnd');
B=A(:,2);
B=B(6001:1:7000,1);
B=B';
B=(B+1)/2;