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- W68360596 abstract "The present paper describes the back-propagation learning of a partial functional differential equation with reaction-diffusion term. The time-dependent recurrent learning algorithm is developed for a delayed recurrent neural network with the reaction-diffusion term. The proposed simulation methods are illustrated by the back-propagation learning of continuous multilayer Hopfield neural network with a discrete time delay and reaction-diffusion term using the prey-predator system as a teacher signal. The results show that the continuous Hopfield neural networks are able to approximate the signals generated from the predator-prey system with Hopf bifurcation." @default.
- W68360596 created "2016-06-24" @default.
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- W68360596 date "2014-01-01" @default.
- W68360596 modified "2023-10-06" @default.
- W68360596 title "Back-Propagation Learning of Partial Functional Differential Equation with Discrete Time Delay" @default.
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- W68360596 doi "https://doi.org/10.1007/978-3-319-10554-3_18" @default.
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