Signal Classification with uniformly distributed values


Tip
When preparing a training case it is always recommended to include as many training cases as possible. However, it is more important to include training cases that represent completely the experiment at hand.

Problem 1
Create a New Project called Uniform (You must select: Multi-layer Network and Classification in the New Project Dialog) for the classification of four signals. Edit the BuildTrainSet.lab file to build an appropriate training set for the classification of four noisy signals: the sine, the saw tooth, the triangular, and the squared. These signals will be contaminated with 10% of noise. The input training set must include 400 waves of each class and 500 additional cases for the sinc function for rejection. Use 2100 training cases and 64 inputs. In previous problems a random phase was used for each signal, in this problem use a uniformly distributed phase in the range from 0 to 2π for each signal. For instance, suppose that you have 10 training case only, thus each training case will start at 0.0, 0.628, 1.256,... ,5.652 radians respectively. Note that the training set target has only four columns.

trainSetInput0

trainSetInput25

trainSetInput50

trainSetInput75

trainSetInput100

trainSetInput125

trainSetInput150

trainSetInput175

trainSetInput200

trainSetInput399

trainSetInput400

Problem 2
Edit the BuilValidSet.lab file to build an appropriate validation set for classification of the noisy four signals. Use 950 validation cases (200 cases for each class and 150 for rejection). The validation set must contain signals contaminated with 10% of noise. Use a random phase for the validation set.

Problem 3
Edit the Train.lab file to design and train an ANN for classification of the four signals.

Problem 4
Edit the CheckTraining.lab file to check the training: (a) Compute the confusion matrix for the ANN using the training set. (b) Plot the error for each network output. (c) Save the confusion matrix as a vector image (trainConf.emf).

Problem 5
Edit the Validation.lab file to perform the validation of the ANN. (a) Compute the confusion matrix for the ANN using the validation set. (b) Plot the error for each network output. (c) Save the confusion matrix as a vector image (validConf.emf).

Problem 6
Generate a report in Microsoft Word. Write some conclusions in the report focusing on the problems that were faced during the simulation and how these problems were or could be solved.

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