Signal Classification: sine wave, sawtooth, triangular, squared and sinc.


Problem 1
Create a New Project called FiveSignal to classify five signals (You must select: Multi-layer Network and Classification in the New Project Dialog). Edit the BuilTrainSet.lab file to build an appropriate training set for the classification of five signals: the sine, the saw tooth, the triangular, the squared and the sinc (see figure below). The total number of training case must be 2000. The ANN must have 64 inputs. The training set input must include 400 waves of each class. Each training case has a wave with random phase. Note that the sinc function must be treated especially because it is not periodic.

Classification

Tip
If necessary, you may adjust the experiment parameters such as: number of training case, number of neurons in the hidden layer, etc.

Problem 2
Edit the BuilValidSet.lab file to build an appropriate validation set for classification of the five signals. Use 1000 validation cases.

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