Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks
- Jie Zhang
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In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are 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. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of 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 via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.
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- Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks
International Journal of Automation and Computing
Volume 3, Issue 1 , pp 1-7
- Cover Date
- Print ISSN
- Online ISSN
- Institute of Automation, Chinese Academy of Sciences
- Additional Links
- Optimal control
- batch processes
- neural networks
- multi-objective optimisation
- Jie Zhang (1)
- Author Affiliations
- 1. School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK