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- W2394242008 abstract "In this paper,an efficient learning algorithm was proposed for a class of fuzzy Hopfield networks(Max-T FHNNs) based on T-norms.For any given set of patterns,the learning algorithm can find the maximum of all connection 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 equilibrium 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." @default.
- W2394242008 created "2016-06-24" @default.
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- W2394242008 date "2010-01-01" @default.
- W2394242008 modified "2023-10-16" @default.
- W2394242008 title "Learning Algorithm and Properties on a Class of Fuzzy Hopfield Networks" @default.
- W2394242008 hasPublicationYear "2010" @default.
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