Nonlinear dynamical systems control using a new RNN temporal learning strategy
Nonlinear dynamical systems control using a new RNN temporal learning strategy
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DOI：
10.1109/tcsii.2005.852191
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发表时间：
2005-11
期刊：
影响因子：
--
通讯作者：
Yong Fang;T. Chow
中科院分区：
文献类型：
--
作者：
Yong Fang;T. Chow
讨论了递归神经网络（RNN）通过其自身的时间操作来处理时变输入/输出的能力。提出了一类新的连续时间（CT）RNN，并证明了给定的n维动态CT系统的任意有限时间轨迹都可以用RNN输出单元的内部状态来近似.所提出的RNN扩展为时间处理。
The ability of recurrent neural networks (RNN) to handle time-varying input/output through its own temporal operation is discussed. A new class of continuous-time (CT) RNN is proposed and it is proved that any finite time trajectory of a given n-dimensional dynamical CT system with input can be approximated by the internal state of the output units of an RNN. The proposed RNNs are extended for temporal processing.