Title :
Nonlinear dynamical systems control using a new RNN temporal learning strategy
Author :
Fang, Yong ; Chow, Tommy W S
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, China
Abstract :
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.
Keywords :
continuous time systems; learning (artificial intelligence); multidimensional systems; nonlinear dynamical systems; recurrent neural nets; temporal reasoning; time-varying systems; 2D system theory; continuous-time recurrent neural networks; finite time trajectory; n-dimensional dynamical continuous-time system; nonlinear dynamical systems control; temporal learning; temporal operation; temporal processing; time-varying input; time-varying output; Control systems; Iterative algorithms; Network topology; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Time varying systems; Two dimensional displays; Continuos-time recurrent neural networks (RNNs); temporal processing; two-dimensional (2-D) system theory;
Journal_Title :
Circuits and Systems II: Express Briefs, IEEE Transactions on
DOI :
10.1109/TCSII.2005.852191
