Enhancing the structure and parameters of the centers for BBF Fuzzy Neural Network classifier construction based on data structure
Tarek M. Hamdani, Adel M. Alimi, Fakhri O. Karray · 2008
This paper aims at presenting different strategies for the construction of beta basis function (BBF) fuzzy neural network. These strategies lead to the determination of the network architecture by determining the structure of the hidden layer and parameters of its centers based on data structure. For that, we use self organizing maps (SOM) clustering to construct a mapped structure of the real training data. By analyzing this structure, we proceed to neuron selection. Data sets were also analyzed with the fuzzy c-means (FCM) clustering technique to generate fuzzy membership values presenting fuzzy outputs for our fuzzy neural model. We propose to estimate the parameters of beta basis function in order to obtain better data coverage. Experimental results show that the use of the proposed technique produces better results.