Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. This paper presents using bootstrap aggregated extreme learning machine for the on-line re-optimisation control of a fed-batch fermentation process. In order to overcome the difficulty in developing mechanistic model, data driven models are developed using extreme learning machine (ELM). ELM has the advantage of fast training in that the hidden layer weights are randomly assigned. A single ELM model can lack of robustness due the randomly assigned hidden layer weights. To overcome this problem, multiple ELM models are developed from bootstrap re-sampling replications of the original training data and are then combined. In addition to enhanced model accuracy, bootstrap aggregated ELM can also give model prediction confidence bounds. A reliable optimal control policy is achieved by means of the inclusion of model prediction confidence bounds within the optimisation objective function to penalise wide model prediction confidence bounds which are associated with uncertain predictions as a consequence of plant model-mismatch. Finally, in order to deal with process disturbances, an on-line re-optimisation strategy is developed and successfully implemented.
Author(s): Baron CMC, Zhang J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics
Year of Conference: 2017
Online publication date: 28/07/2017
Acceptance date: 02/04/2016
Library holdings: Search Newcastle University Library for this item