Concerning the learning problems of recurrent neural networks (RNNs), this paper deals with the problem of approximating a dynamical system (DS) by an RNN as one extension of the problem of approximating trajectories by an RNN. In particular, we systematically investigate how an RNN can produce a DS
Neural networks and dynamical systems
β Scribed by Kumpati S. Narendra; Kannan Parthasarathy
- Publisher
- Elsevier Science
- Year
- 1992
- Tongue
- English
- Weight
- 965 KB
- Volume
- 6
- Category
- Article
- ISSN
- 0888-613X
No coin nor oath required. For personal study only.
β¦ Synopsis
Models for the identification and control of nonlinear dynamical systems using neural networks were introduced by Narendra and Parthasarathy in 1990, and methods for the adjustment of model parameters were also suggested. Simulation results of simple nonlinear systems were presented to demonstrate the feasibility of the schemes proposed. The concepts introduced at that time are investigated in this paper in greater detail. In particular, a number of questions that arise when the methods are applied to more complex systems are addressed. These include nonlinear systems of higher order as well as multivariable systems. The effect of using simpler models for both identification and control are discussed, and a new controller structure containing a linear part in addition to a multilayer neural network is introduced.
π SIMILAR VOLUMES
## Abstract In order to obtain a reliable model of power systems, identification of power system dynamics by employing a neural network is studied. A new method of combined use of a mathematical model and a neural network is proposed. The effectiveness of the proposed method is verified by applying