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- W2117067024 abstract "The self-organization of a nonlinear, single-layer neural network is mathematically analyzed, in which a regular Hebbian rule and an anti-Hebbian rule are used for the adaptation of the connection weights between the constituent units. It is shown that the equilibrium points of this system are global asymptotically stable. Following some restrictive assumptions a nonlinear principal component analyzer can be constructed." @default.
- W2117067024 created "2016-06-24" @default.
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- W2117067024 date "2002-11-22" @default.
- W2117067024 modified "2023-09-27" @default.
- W2117067024 title "Global stability analysis of a nonlinear principal component analysis neural network" @default.
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- W2117067024 doi "https://doi.org/10.1109/icnn.1997.614167" @default.
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