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
Control of nonlinear dynamical systems modeled by recurrent neural networks
β Scribed by Michael Nikolaou; Vijaykumar Hanagandi
- Publisher
- American Institute of Chemical Engineers
- Year
- 1993
- Tongue
- English
- Weight
- 323 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0001-1541
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In this study we composed a recurrent neural network learning controller and applied it to the swinging up and stabilization problem of the inverted pendulum. A recurrent neural network was trained by a genetic algorithm which had an internal copy operator or inter-individual copy operator. An appro
A new paradigm called self-recurrent neural network (SRNN) is proposed. Two SRNNs are utilized in a control system, one as an emulator and the other as a controller. To guarantee convergence and for faster learning, an approach using adaptive learning rate is developed by Lyapunov function. Finally,