Back-Propagation Learning of Partial Functional Differential Equation with Discrete Time Delay
T Kmet, M Kmetova - … : 16th International Conference, AIMSA 2014, Varna …, 2014 - Springer
T Kmet, M Kmetova
Artificial Intelligence: Methodology, Systems, and Applications: 16th …, 2014•SpringerThe 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 …
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 …
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.
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