Identification and control of dynamical systems using neural networks
- 1 March 1990
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 1 (1) , 4-27
- https://doi.org/10.1109/72.80202
Abstract
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described.Keywords
This publication has 19 references indexed in Scilit:
- Qualitative analysis and synthesis of a class of neural networksIEEE Transactions on Circuits and Systems, 1988
- Learned classification of sonar targets using a massively parallel networkIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Layered neural nets for pattern recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Nonlinear decoupling via feedback: A differential geometric approachIEEE Transactions on Automatic Control, 1981
- Stable discrete adaptive controlIEEE Transactions on Automatic Control, 1980
- Global stability of parameter-adaptive control systemsIEEE Transactions on Automatic Control, 1980
- Discrete-time multivariable adaptive controlIEEE Transactions on Automatic Control, 1980
- Decoupling in a Class of Nonlinear Systems by State Variable FeedbackJournal of Dynamic Systems, Measurement, and Control, 1972
- Optimization of time-varying systemsIEEE Transactions on Automatic Control, 1965
- Multiparameter self-optimizing systems using correlation techniquesIEEE Transactions on Automatic Control, 1964