| Evolving Neural Networks for Chlorophyll-a Prediction (2009) | |||||||||||||||
Abstract | |||||||||||||||
| This paper studies the application of evolutionary arti cial neural networks to chlorophyll-a prediction in Lake Kasumigaura. Unlike previous applications of arti cial neural networks in this eld, the architecture of the arti cial neural network is evolved automatically rather than designed manually. The evolutionary system is able to nd a near optimal architecture of the arti cial neural network for the prediction task. Our experimental results have shown that evolved arti cial neural networks are very compact and generalise well. The evolutionary system is able to explore a large space ofpossible arti cial neural networks and discover novel arti cial neural networks for solving aproblem. 1 | |||||||||||||||
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