Evolutionary multiobjective design of radial basis function networks for greenhouse environmental control
In this work a multiobjective genetic algorithm is applied to the identi cation of radial basis function neural network coupled models of humidity and temperature in a greenhouse. Models are built as one-step-ahead predictors and then used iteratively to produce long term predictions. The number of...
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| Other Authors: | , |
| Format: | conferenceObject |
| Language: | eng |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.1/2344 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2344 |
| Summary: | In this work a multiobjective genetic algorithm is applied to the identi cation of radial basis function neural network coupled models of humidity and temperature in a greenhouse. Models are built as one-step-ahead predictors and then used iteratively to produce long term predictions. The number of neurons and input terms used in both models de ne the search space. Two combinations of performance and complexity criteria are used to steer the selection of model structures, resulting in distinct sets of solutions. It is shown that minimisation of one-step-ahead prediction errors negatively in uences long term prediction performance. Long term prediction results are presented for a pair of models selected from sets of models obtained in the experiments. |
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