Optimal Parameter Selection Using Three-term Back Propagation Algorithm for Data Classification

The back propagation (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural Network. It has been successfully deployed in numerous practical problems and disciplines. Regardless of its popularity, BP is still known for some major drawbacks such as easily getting stuck in local minima and slow convergence; since, it uses Gradient Descent (GD) method to learn the network. Over the years, many improved modifications of the BP learning algorithm have been made by researchers but the local minima problem remains unresolved. Therefore, to resolve the inherent problems of BP algorithm, this paper proposed BPGD-A3T algorithm where the approach introduces three adaptive parameters which are gain, momentum and learning rate in BP. The performance of the proposed BPGD-A3T algorithm is then compared with BPGD two term parameters (BPGD-2T), BP with adaptive gain (BPGD-AG) and conventional BP algorithm (BPGD) by means of simulations on classification datasets. The simulation results show that the proposed BPGD-A3T shows better performance and performed highest accuracy for all dataset as compared to other.