Modified Fuzzy-CMAC Networks with Clustering-based Structure

This work proposes a modified structure for the fuzzy-CMAC network to solve the curse of dimensionality problem, observed when systems with a high number of involved variables are being modeled with the aid of computational intelligence techniques. The approach is based on the fuzzy C-means clustering algorithm, which is used here to initialize the CMAC fuzzy input partitions. Also, the number of fuzzy-CMAC internal memories is drastically decreased by using the clusters defined by the fuzzy C-means algorithm. During network training phase, those fuzzy input partitions are adjusted together with the output layer weights of the network by means of the backpropagation algorithm. To assess the effectiveness of the modified fuzzy-CMAC network structure, the thermal modeling of a real world tube laminating process with nine input variables was accomplished. Obtained results are considered satisfactory when compared with those of classical artificial neural networks algorithms like an adaptive neuro-fuzzy inference system (ANFIS) network and a multilayer perceptron (MLP) network.