Abstract: Symmetric polygonal fuzzy number is employed to build an improved feedforward fuzzy neural network (FNN). First, a novel fuzzy arithmetic and extension principle for the polygonal fuzzy numbers is derived. Second, the topological architecture of a three layer feedforward FNN is presented, and the input-output law of this network is systematically studied. Third, a fuzzy BP learning algorithm for the polygonal fuzzy number connection weights and thresholds of the FNN is developed. Finally a simulation example is illustrated to analyze the adaptive three layer feedforward FNN to realize data pairs consisting of real numbers and symmetric polygonal fuzzy numbers, approximately. Keywards: algorith
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