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Design of Simple ANN (SANN) model for Data Classification and its performance Comparison with FLANN (Functional Link ANN)


Gunanidhi Pradhan, Vishal Korimilli, Suresh Chandra Satapathy, Sabyasachi Pattnaik, Bhabatosh Mitra


Vol. 9  No. 10  pp. 105-115


Data Classification is a prime task in Data mining. Accurate and simple data classification task can help the clustering of large dataset appropriately. In this paper we have experimented and suggested a simple ANN based classification models called as Simple ANN (SANN) for different classification problems. The GA is used for optimally finding out the number of neurons in the single hidden layered model. Further, the model is trained with Back Propagation (BP) algorithm and GA (Genetic Algorithm) and classification accuracies are compared. The designed models are also compared with the Functional Link ANN (FLANN) for Data classification accuracies. It is revealed from the simulation that our suggested model is performing better compared to FLANN model and it can be a very good candidate for many real time domain applications as these are simple with good performances.


ANN, Genetic Algorithm, Data classification, FLANN