Abstract
In this paper, we proposed a novel multi-input and single-output (MISO) polygonal fuzzy neural network based on polygonal fuzzy numbers and their extension operations. A uniformity analysis of this network is provided for cases where an activation function is nonnegative continuous and monotonous. In order to avoid the complex calculation of the partial derivatives of error functions related to connection weights and threshold values, we use the Hebb rule and the particle swarm algorithm to design two kinds of optimal learning algorithms to handle these two calculations, respectively. Using a simulation example, we analyzed and compared the two kinds of optimization algorithms in detail. The results showed that our optimization algorithms based on MISO fuzzy neural networks exhibited randomness and parameters diversity, but the Hebb algorithm was simple and easy to realize. The particle algorithm was stable and converged quickly
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