Evolutionary Multiobjective Design of Radial Basis Function Networks for Greenhouse Environmental Control
| Authors: | Ferreira Pedro M., University of Algarve, Centre for Intelligent Systems, Portugal
Ruano António E., University of Algarve, Centre for Intelligent Systems, Portugal
Fonseca Carlos M., University of Algarve, Centre for Intelligent Systems, Portugal |
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| Topic: | 2.4 Optimal Control |
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| Session: | Evolutionary Computation in Control Systems Engineering |
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| Keywords: | Genetic Algorithms, Greenhouse Environmental Control, Radial Basis Functions, Temperature Prediction, Humidity Prediction |
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Abstract
In this work a multiobjective genetic algorithm is applied to the identification 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 define 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 influences long term prediction performance. Long term prediction results are presented for a pair of models selected from sets of models obtained in the experiments.
