Supervised learning for Neural Network using Ant Colony Optimization
Ravinder Rathee, Anita Dagar, Seema Rani · 2014
To describe the approach of real-world activities we have proposed an idea of SLNA algorithm and its diagram. In this paper we are using supervised learning to train the network. In supervised learning desire response is provided by the teacher in correspondence to the particular input. To explain the concept of SLNNA algorithm we have used a real-world example of travel agency (make my trip agency). To optimize the path in the search space, we have used ATSP algorithm.