NEURAL NETWORKS IN THE MANAGEMENT OF COMPLEX TECHNICAL SYSTEMS

  • R.L Panteev
Keywords: artificial intelligence, neural network, control system, error, feedback, dynamic object, automatic control, adaptive control.

Abstract

The methods of application of neural networks for solving the problems of control of dynamic objects are considered. For each type of neural control, the circuits for connecting the neural networks within the control system are presented and the procedures for their training are described in detail. The advantages and disadvantages of the described methods are analyzed.

References

Li Y. (2001) Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks. Automatica. Vol. 37. N 8. P. 1293 – 1301.

Gundy-Burlet K. (2004) Augmentation of an Intelligent Flight Control System for a Simulated C-17 Aircraft. J. of Aerospace Computing, Information, and Communication. Vol. 1, N 12. P. 526 – 542.

Prokhorov D. (1997) Adaptive Critic Designs. IEEE Transactions on Neural Networks. Vol. 8, N 5. P. 997 – 1007.

Arhangelskij V.I. (1999) Nejronnye seti v sistemah avtomatizacii. Kyiv: Tehnika.

Published
2019-06-14
How to Cite
PanteevR. (2019). NEURAL NETWORKS IN THE MANAGEMENT OF COMPLEX TECHNICAL SYSTEMS. Herald of Kyiv Institute of Business and Technology, 40(2), 68-72. https://doi.org/10.37203/kibit.2019.40.16