Active learning for nonlinear system identification and control

Tirthankar Raychaudhuri, Leonard Hamey

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


The identification and control of nonlinear systems continues to remain a challenging issue. Neural network models and controllers have often been effective in addressing the situation. However current neural network learning methods have been found to be limited in their generalisation abilities. Recent research has shown active learning methods to be effective in increasing the modelling reliability of a neural network system. An active learning agent has the ability to query its environment in order to make a selection of its training data. One approach to the implementation of active leaning is to use `querying-by-committee'. This results in considerably reduced data collection and at the same time does not compromise the accuracy of identification. A nonlinear plant with both clean and noisy data is successfully modelled by such a technique and a feedforward neural network controller based upon such a model is demonstrated to perform effectively.
Original languageEnglish
Title of host publicationProceedings of the 13th World Congress : International Federation of Automatic Control, San Francisco, USA, 30th June-5th July 1996
EditorsJanos J. Gertler, Jose B. Cruz, Jr., Michael Peshkin
Number of pages5
ISBN (Print)0080426050
Publication statusPublished - 1996
EventInternational Federation of Automatic Control. World Congress (13th : 1996) - San Francisco, CA, United States
Duration: 30 May 19965 Jul 1996


ConferenceInternational Federation of Automatic Control. World Congress (13th : 1996)
Country/TerritoryUnited States
CitySan Francisco, CA


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