Simple. Use the neural network toolbox. First you should use a matrice to save the input data.
For eg: If you have 3 classes say A,B and C and if you have 5 samples for every class then you should do the following
Create two matrices input and output.
Each column of the input matrice correspond to a single class.
Let the 5 samples of class A be a1, a2 and similarly for class B and C.
input=[ a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 c1 c2 c3 c4 c5];
The target matice should be as follows
As there are 3 classes we should have 3 rows. For the corresponding input you should define the corresponding class in the target matrix
target=[ 1 0 0 ; 1 0 0 ; 1 0 0 ; 1 0 0 ; 1 0 0; 0 1 0; 0 1 0; and so on]
Transpose the above target matrix so that each column corresponds to a single sample
Here for the sample a1 we have set 1 0 0 which means that it belongs to class A.
Similarly for the sample b1 we have set 0 1 0 which means that it belongs to class B.
Then you open the Pattern Recognition Toolbox of the Matlab. It's easy from there..
Note: Increasing the no of samples of the input always increases the accuracy.
Thanks
---------- Post added at 19:40 ---------- Previous post was at 19:38 ----------
If you aren't good at the basis of the neural networks then I recommend you to go to Matlab Central. There you can learn a lot about neural networks using Matlab.
---------- Post added at 19:41 ---------- Previous post was at 19:40 ----------
hello,
im doing speaker recognition project. I already processed the input signal coming from my microphone,and done it with 13th coefficient of MFCC(mel-frequency cepstrum coeeficient). For recognition part, i decide to use neural network but i had problems with that part which is:
1)What the input should be in my neural network?
Please help me..
What are you doing? Speaker recognition or Speech recognition? I have a code for Speaker recognition.