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This paper proposes a two-phase identification approach to Mamdani fuzzy neural networks. The first phase is the system identification which includes a novel forward recursive input-output clustering method for the structure initialization and the gradient descent algorithm for the parameter initialization. The main advantage of the proposed method is that it fits perfectly the special clustering...
An algorithm for the generation of a TS-type neuro-fuzzy system is presented. There are two stages in the generation: in the first stage, an initial structure adapted from an empty neuron or fuzzy rule set, based on the geometric growth criterion and the ɛ-completeness of fuzzy rules; in the second stage, the obtained initial structure is refined by a hybrid learning algorithm based on backpropagation...
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