On the Synergism of Evolutionary Neuro-Fuzzy System

In the recent past, it has been seen that the synergism of evolutionary, fuzzy and neural network is gaining popularity over individual techniques due to its combined computational efficiency. In this paper, we have investigated the feasibility of synergism between evolutionary fuzzy clustering using three different validity criteria and neural networks. We have also reported the effect of various parameters such as fuzzifier, outlier control and generalization parameter over system performance. Here, all the variants of evolutionary fuzzy clustering are employed for structure selection and learning of neural network. Performance evaluation has been carried out over wide spectrum of benchmark problems and biometric recognition problems. Experimental results demonstrate the comparative analysis of synergism of evolutionary fuzzy clustering with neural network. It has been found that evolutionary fuzzy clustering using Xie Beni criteria with neural network outperforms over other variants. Also, evolutionary fuzzy clustering combined with neural network performs far better than the synergism of fuzzy clustering with neural network. We have obtained promising results even for biometric datasets including eye-movement, face and periocular biometrics.