Performance evaluation of fuzzy neural network with various aggregation operators

The modified fuzzy neural network (MFNN) proposed by Kulkarni and Sontakke is an extension of the fuzzy neural network (FNN) proposed by Kwan and Cai. Unlike FNN, the MFNN uses the Yager class of fuzzy union and intersection operators and works under a supervised environment. The paper describes MFNN with its learning algorithm. The MFNN is extended further and its performance is verified using various fuzzy aggregation operators. It is observed that Dubois and Prade operators give highest recognition rates, as compared to other operators for Fisher Iris data. Classification performance of the Hamacher operator is better with less number of neurons for Fisher Iris data, whereas the performance with the min-max operator for Wine data is better. Timing analysis is also performed and training time is found nearly equal for all the operators. The recall time per pattern is significantly less in the case of Schweizer and Sklar, and Hamacher operators. Thus, instead of using min-max or Yager class of operators one can tune the performance of the MFNN classifier to improve generalization performance by proper selection of aggregation operators.