Author : P.Brundha, K.Ramanathan and K.Rangarajan

A Fuzzy Neural Network (FNN) model, combining both the fuzzy set and neural network theories, has been developed aiming to improve the objectives of I.T professionals’ analytical skills and prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Some cases with detailed decision criteria for prequalifying the I.T Professionals were collected. These cases were used for training and testing the FNN model in their Project-Management. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feed forward neural network (GFNN, (i.e.) a crisp neural network) approach. These results indicate the applicability of the neural network approach for I.T professionals prequalification and the benefits of the FNN model over the GFNN model.

Key Words: Fuzzy reasoning, Neural network, I.T Professionals prequalification

Article published in International Journal of Current  Engineering  and Technology, Vol.3,No.2 (June- 2013)