基于布谷鸟搜索的多层感知器在洪水水位预测中的应用

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI:10.1109/IJCNN.2016.7727243

Suwannee Phitakwinai, S. Auephanwiriyakul, N. Theera-Umpon

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引用次数: 10

摘要

采用布谷鸟搜索(CS)算法的前馈多层感知器(MLP) CS-MLP，对泰国清迈市区平河7小时前水位进行预测。将CS-MLP模型的预测性能与常规多层感知器(MLP)和前人的研究结果进行了比较。其中，CS-MLP在盲测数据集上的平均绝对误差为6.836 cm，是最好的。

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Multilayer perceptron with Cuckoo search in water level prediction for flood forecasting

The feed forward multilayer perceptron (MLP) with the Cuckoo search (CS) algorithm, called CS-MLP is implemented to predict 7-hours-ahead water level of the Ping river at the downtown area of Chiang Mai, Thailand. The CS-MLP model prediction performance is compared with the regular multilayer perceptron (MLP) and the results from the previous work. The CS-MLP is the best among them with the mean absolute error on the blind test data set of 6.836 cm.

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2016 International Joint Conference on Neural Networks (IJCNN)

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