Computer Science ›› 2010, Vol. 37 ›› Issue (12): 206-208.

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Learning Algorithm and Properties on a Class of Fuzzy Hopfield Networks

ZENG Shui-ling,YANG Jing-yu,XU Wei hong   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In this paper, an efficient learning algorithm was proposed for a class of fuzzy Hopfield networks(Max-T FHNNs) based on I=norms. For any given set of patterns, the learning algorithm can find the maximum of all conneclion weight matrices that can make the set become a set of the equilibrium points of the Max-T FHNN when T is a left continuous T-norm. This maximal matrix is idempotent matrix in sense of Max-T composition, with which the Max-T FHNN can be convergent to a stable state in one iterative process for any input vector. It is proved theoretically that arbitrary set of patterns can become a set of the ectuilibrium points of every Max-T FHNN if only the T is left continuous T-norm. Max-TL FHNN has universally good robustness to perturbation of training pattern.

Key words: Hopfield network, Equilibrium point, Learning algorithm, Stability, Robustness

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