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Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

Lookup NU author(s): Dr Jie ZhangORCiD


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A recurrent neuro-fuzzy network based strategy for batch process modelling and multi-objective optimal control is presented. In this recurrent neuro-fuzzy network a "global" nonlinear long-range prediction model is constructed from the fuzzy conjunction of a number of "local" linear dynamic models. The network output is fed back to the network input through one or more time delay units. This particular structure ensures that predictions from a recurrent neuro-fuzzy network are long-range or multi-step-ahead predictions. Process knowledge is used to initially partition the process nonlinear characteristics into several local operating regions and to aid in the initialisation of the corresponding network weights. Process input output data is then used to train the network. Membership functions of the local regimes are identified and local models are discovered through network training. In this paper, a recurrent neuro-fuzzy network is used to model a fed-batch reactor and to calculate the optimal feeding policy.

Publication metadata

Author(s): Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Proceedings of the International Joint Conference on Neural Networks

Year of Conference: 2003

Pages: 304-309

ISSN: 1098-7576

Publisher: IEEE


DOI: 10.1109/IJCNN.2003.1223362

Library holdings: Search Newcastle University Library for this item

ISBN: 0780378989