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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jie Zhang |
| Copyright Year | 2003 |
| Description | Author affiliation: Sch. of Chem. Eng. & Adv. Mater., Newcastle Univ., Newcastle upon Tyne, UK (Jie Zhang) |
| Abstract | 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. |
| Sponsorship | Int. Neural Network Soc. IEEE Neural Networks Soc |
| Starting Page | 304 |
| Ending Page | 309 |
| File Size | 370476 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780378989 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2003.1223362 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-07-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Optimal control Fuzzy neural networks Predictive models Neural networks Inductors Process control Delay effects Manufacturing processes Polymers Chemical analysis |
| Content Type | Text |
| Resource Type | Article |
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