Fully parallel on-chip learning hardware neural network for real-time control

Published

Journal Article

A parallel hardware neural network with on-chip learning ability is presented. The chip is used to perform real-time output feedback control on a nonlinear dynamic system. The non linear plant is a simulated unstable combustion process and is nonlinear enough that linear controllers give poor performance. Neural networks provide an adaptive sub-optimal control that does not need any prior knowledge of the system. The hardware neural network presented here utilizes parallelism to achieve speed independent of the size of the network, enabling real-time control. Parallel on-chip learning ability allows the hardware neural network to learn on-line as the plant is running and the plant parameters are changing. The experimental setup used to show that the parallel hardware learning neural network chip can control the simulated combustion system is described, and the results discussed.

Duke Authors

Cited Authors

  • Liu, J; Brooke, M

Published Date

  • January 1, 1999

Published In

Volume / Issue

  • 5 /

International Standard Serial Number (ISSN)

  • 0271-4310

Citation Source

  • Scopus