A voting-mechanism-based fuzzy neural network system is proposed in this paper. When constructing the network structure, a generalized class cover problem is presented and its two solving algorithm, an improved greedy algorithm and a binary particle swarm optimization algorithm, are proposed to get the class covers with relatively even radii, which are used to partition fuzzy input space and extract fewer robust fuzzy IF-THEN rules. Meanwhile, a weighted Mamdani inference mechanism is adopted to improve the efficiency of the system output and a real-valued particle swarm optimization-based algorithm is used to refine the system parameters. Experimental results show that the system is feasible and effective. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Huang, Y., Wang, Y., Zhou, W., Yu, Z., & Zhou, C. (2005). A Fuzzy neural network system based on generalized class cover and particle swarm optimization. In Lecture Notes in Computer Science (Vol. 3645, pp. 119–128). Springer Verlag. https://doi.org/10.1007/11538356_13
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