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Learning the trajectories of periodic attractor using recurrent neural network
应用递归神经网络学习周期运动吸引子轨迹

Keywords: recurrent neural network,periodic attractor,generalization ability
递归神经网络
,周期吸引子,泛化能力

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Abstract:

A kind of RNN(recurrent neural network) is applied to the learning of periodic attractor trajectories for nonlinear system. The network topology is based on the state-space representation, and the network parameters are optimized by the back-propagation through time algorithm. Investigations are then conducted into the model performance influenced by different training trajectories and different structure parameters. The model evaluation rule is based on multi-trajectory, which makes the investigation more objective. Simulation results from the van der Pol system show that the generalization ability is dependent on the training trajectory, different trajectories result in a significant different prediction performance; Simulation results also show that the structure parameters of the neural network should be carefully chosen so that better generalization ability can be obtained.

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