International Journal of Emerging Technology and Advanced Engineering (IJETAE)
Artificial Neural networks have emerged as an important tool for classification. Many neural network models have been proposed for pattern classification, function approximation and regression problems. Among them, the class of multi-layer per-ceptron networks is most popular. This paper presents an approach for classifying a person as below poverty line or not using Multilayer feed forward neural networks. This network is trained by varying different parameters like learning rate and number of iterations for determining performance level of the network constructed and it describes which training functions and learning parameters are suitable for the application of BPL(Below Poverty Line) classification.