  In case of decision making problems, classification of pattern is a complex
and crucial task. Pattern classification using multilayer perceptron (MLP)
trained with back propagation learning becomes much complex with increase in
number of layers, number of nodes and number of epochs and ultimate increases
computational time [31]. In this paper, an attempt has been made to use fuzzy
MLP and its learning algorithm for pattern classification. The time and space
complexities of the algorithm have been analyzed. A training performance
comparison has been carried out between MLP and the proposed fuzzy-MLP model by
considering six cases. Results are noted against different learning rates
ranging from 0 to 1. A new performance evaluation factor 'convergence gain' has
been introduced. It is observed that the number of epochs drastically reduced
and performance increased compared to MLP. The average and minimum gain has
been found to be 93% and 75% respectively. The best gain is found to be 95% and
is obtained by setting the learning rate to 0.55.

Neural and Evolutionary Computing