| Problem 1 |
| Create a New Project called TraingularWave to build an appropriate training set for learning of the triangular signal z = triang(x) using an ANN for auto-association. Use 2048 training cases and 64 inputs. The input training set must include noisy triangular waves and the target must include clean triangular waves as shown in the figure. The training set must include each phase in eight training cases. |
| Problem 2 |
| Edit the BuilValidSet.lab file to build the validation set using the training set. Contaminate the target training set with 10% of noise to create the validation set input. The target of the validation set is the same as the target of the training set. |
| Problem 3 |
| Edit the Train.lab file to design and train an ANN for the triangular wave. |
| Problem 4 |
| Edit the CheckTraining.lab file to check the training: (a) Compute the mean squared error for the ANN using the training set. (b) Plot the error for each training case. (c) Save the plot as a vector image (checkTraining.pdf and checkTraining.emf) (d) Double click the variable "output" to evaluate the performance of the network. |
| Problem 5 |
| Edit the Validation.lab file to perform the validation of the ANN. (a) Compute the mean squared error for the ANN using the validation set. (b) Plot the error for each validation case. (c) Save the plot as a vector image (validation.pdf and validation.emf) (d) Double click the variable "output" to evaluate the performance of the network. |
| 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. |