An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

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This paper presents a hybrid neural network, called the self-organising fuzzy neural network (SOFNN), to extract fuzzy rules from the training data. The first hidden layer of this network consists of ellipsoidal basis function (EBF) neurons. Every EBF neuron in the SOFNN has both a centre vector and a width vector. Neurons are organised by the network itself. The methods of the structure and parameter learning, based on new adding and pruning techniques and a recursive learning algorithm, are simple and effective, with a high accuracy and a compact structure. Simulations show that the SOFNN has the capability to encode fuzzy rules in the resulting network.