Classification using Adaptive Fuzzy Inference Neural Networks

P. Jahankhani, V. Kodogiannis, K. Revett (UK), and J. Lygouras (Greece)


Neuro-fuzzy systems, fuzzy clustering, recursive least squares estimation.


The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The convergence of training is faster because the least-squares algorithm is applied to the estimation of consequence parameters of the system and backpropagation is applied only to the estimation of the premise parameters. Using the proposed scheme, high-dimensional fuzzy systems can be realized with fewer rules than a typical Takagi-Sugeno fuzzy system. A number of simulations demonstrate the performance of the proposed system

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