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Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-Fuzzy Networks

Lookup NU author(s): Dr Jun Zhang


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Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy network is presented. The recurrent neuro-fuzzy network forms a “global” nonlinear long-range prediction model through 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 and this structure ensures that predictions from a recurrent neuro-fuzzy network are long-range predictions. In building a recurrent neural network model, 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 operational data is then used to train the network. Membership functions of the local regimes are identified and local models are discovered through network training. Based on the recurrent neuro-fuzzy network model, multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

Publication metadata

Author(s): Zhang J

Publication type: Article

Publication status: Published

Journal: International Journal of Automation and Computing

Year: 2006

Volume: 3

Issue: 1

Pages: 1-7

ISSN (print): 1476-8186

ISSN (electronic): 1751-8520

Publisher: Zhongguo Kexue Zazhishe


DOI: 10.1007/s11633-006-0001-4


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