Neural network control using active learning

Tirthankar Raychaudhuri, Leonard Hamey, Rodney Bell

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Neural networks have been shown to give considerably better results when controlling complex non-linear systems than conventional control methods. Most neural network controllers today are built around `passive' learning methods whereby the network once trained is expected to perform repeatedly with equal accuracy on fresh sets of input-output data. This is not always suitable in real world situations where external environmental parameter variations cause changes in the plant and controller performance. In the current paper we propose the use of an autonomous `active' learning technique which will cause training to re-occur precisely when these parameter variations happen, yielding enhanced controller performance.
Original languageEnglish
Title of host publicationControl 95 : meeting the challenge of Asia Pacific growth
Place of PublicationBarton
PublisherInstitute of Engineers Australia
Pages369-373
Number of pages5
ISBN (Print)0858256312
Publication statusPublished - 1995

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Raychaudhuri, T., Hamey, L., & Bell, R. (1995). Neural network control using active learning. In Control 95 : meeting the challenge of Asia Pacific growth (pp. 369-373). Barton: Institute of Engineers Australia.