@article{1331,
  author = {Reddy, G. N., Gurpreet Singh},
  title = {E-Learning Tool for Backpropagation Neural Network Architecture},
  journal = {Progress in Computing Applications},
  year = {2013},
  volume = {2},
  number = {2},
  doi = {},
  url = {http://www.dline.info/pca/fulltext/v2n2/1.pdf},
  abstract = {This paper presents an e-Learning tool for mastering the back-propagation neural network architecture. A short review of the existing tools is presented. It is developed using MS Visual C++. The tool’s functionality can be summarized as: First, at its highest-level, it operates two basic modes: the training mode and the recall mode. Second, while it is in training, it has two sub-modes: the learning-mode and the application-mode. In learning mode, the software generates textoutput traces corresponding to the top-down design steps of the NN-architecture. The generated numeric traces have dualusage, either they can be used learning purposes or for generating class room tests. While in application-training mode, the tool displays only the input-output relations – the values before and after the training. In this mode the tool also generates a cumulative error-index to monitor the progress of the network training. Third, it enables the user to enter the network training termination criteria. Fourth, at the end of the network training, it is stores the trained network into a text-file. Fifth, in the test or recall mode, the trained network is retrieved from a stored-file, it then generates the network response corresponding to the entered test input. The e-Learning tool is tailored for mastering, class room teaching, and test generation of the BP-NN-architecture.},
}