Description
1. The dataset in ‘data_for_cnn.mat’ consists of 1000 ECG signals and each row corresponds to
one ECG signal. The class label for each ECG signal is given in ‘class_label. mat’ file.
Implement the 1D convolutional neural network with BPCNN as the learning algorithm for
the evaluation of optimal weight matrices in FC layers and optimal kernels or filters in
convolution layer. The network consists of one convolutional layer, one pooling layer and
two fully connected (FC) layers. The network flow is given by
Input-Convolution Layer-Pooling layer- Convolution Layer-Pooling layer -FC1-FC2-FC3-Output
Consider the square loss function as cost function in the output layer. You can select number of
hidden neurons by your own choice. In the pooling layer, you can use average pooling with downsampling factor as 2. (For implementation of the BPCNN algorithm, please refer to the class notes or
slides).
(For MATLAB, you can use the inbuilt functions from deep learning toolbox)
(You can use Python with Keras, and tensorflow at the backend.)
2. Implement the 1D convolutional autoencoder for the dataset given in data_for_cnn.mat file.
The architecture is given as
(input-convolution layer-pooling layer-FC-upsampling layer-transpose convolution layer)
(For MATLAB, you can use the inbuilt functions from deep learning toolbox)
(You can use Python with Keras, and tensorflow at the backend.)
3. Implement the neuro-fuzzy inference system (NFIS) classifier for the classification task. You
can use data4.xlsx file. The training and test instances can be selected using hold out crossvalidation.
