| 引用本文: | 韩敏,史志伟,席剑辉.应用递归神经网络学习周期运动吸引子轨迹[J].控制理论与应用,2006,23(4):497~502.[点击复制] |
| HAN Min, SHI Zhi-wei, XI Jian-hui.Learning the trajectories of periodic attractor using recurrent neural network[J].Control Theory and Technology,2006,23(4):497~502.[点击复制] |
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| 应用递归神经网络学习周期运动吸引子轨迹 |
| Learning the trajectories of periodic attractor using recurrent neural network |
| 摘要点击 1344 全文点击 1378 投稿时间：2004-07-02 修订日期：2005-10-25 |
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| DOI编号 |
| 2006,23(4):497-502 |
| 中文关键词 递归神经网络 周期吸引子 泛化能力 |
| 英文关键词 recurrent neural network periodic attractor generalization ability |
| 基金项目 国家自然科学基金资助项目(60374064)． |
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| 中文摘要 |
| 采用递归神经网络学习非线性周期运动的吸引子轨迹．网络的拓扑结构基于非线性系统的状态空间表达式,网络权值通过时序反向传播算法调整．探讨了不同样本轨迹和网络结构对递归神经网络预测性能的影响．神经网络的性能评估建立在多条测试样本轨迹的基础上,可以更为客观地评价递归神经网络预测性能．对van der Pol方程的仿真结果表明:网络的泛化能力对训练样本轨迹的依赖性较强,从不同训练轨迹上得到的递归神经网络性能差异较大;需要选择合适的递归神经网络结构参数以提高神经网络的泛化能力． |
| 英文摘要 |
| 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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