Modelling of a fermentation process using Multi-Layer Perceptrons: Epochs vs Pattern learning, Sigmoid vs Linear transfer function
✍ Scribed by D. Tsaptsinos; J.R. Leigh
- Publisher
- Elsevier Science
- Year
- 1993
- Weight
- 383 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0745-7138
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✦ Synopsis
A number of differently configured Multi-Layer Perceptrons (MLP) were tested and compared for the system modelling of a highly non-linear fermentation process. The MLPs differed with respect to the inputs, the transfer functions, and the weight-updating schemes employed. In this paper it is shown that whereas all MLPs achieved the required error minimization their performances when used as models were greatly diverse. The modelling of the product concentration as a benchmark serves to highlight the benefits to be gained by adopting the configuration suggested by Lapedes and Farber (A. Lapedes and R. Farber 1987. Non-linear signal processing using neural networks: Prediction and system modeling. Los Alamos National Laboratory report, LA-UR-87-2662.) The configurations were tested with both training and previously unseen data. Further experiments with the selected configuration showed that a learning rate of 0.4 resulted in a model that was less sensitive towards the data.