This implementation uses a neural network in order to determine the "type" of a signal. It uses supervised learning ( = Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems— Page 3, Pattern Recognition and Machine Learning, 2006. ) and based on the information it gathered during the training process it analyzes the testing data and provides an ouput.
Database tested : KDD Cup'99 -> 10% out of which 80% was used for training and 20% for testing.
PSO-FLN Implementation.
PreProcessing.
Architecture.
Results
