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控制理论与应用 2006
Learning the trajectories of periodic attractor using recurrent neural network
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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.