Evolving the neural network model for forecasting air pollution time series
Evolving the neural network model for forecasting air pollution time series
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DOI：
10.1016/j.engappai.2004.02.002
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发表时间：
2004-03-01
影响因子：
8
通讯作者：
Kolehmainen, M
中科院分区：
文献类型：
--
作者：
Niska, H;Hiltunen, T;Kolehmainen, M
由于实际过程的混沌和非线性现象以及高维样本空间，对空气质量等真实世界过程的建模通常是一项困难的任务。尽管神经网络(NN)已经在这一领域得到了成功的应用，但在建立符合实际情况的模型时，网络结构的选择仍然是一个问题和耗时的任务。采用并行遗传算法(GA)对赫尔辛基一个繁忙的城市交通站点的二氧化氮小时浓度进行了多层感知器模型的输入选择和高层结构设计。此外，还对遗传算法的参数整定问题进行了实验研究。结果表明，遗传算法是解决神经网络设计实际问题的有效工具。然而，人们注意到，对神经网络模型的评估是一个计算昂贵的过程，这对搜索技术设置了限制。(C)2004爱思唯尔有限公司。保留所有权利。
The modelling of real-world processes such as air quality is generally a difficult task due to both their chaotic and non-linear phenomenon and high dimensional sample space. Despite neural networks (NN) have been used successfully in this domain, the selection of network architecture is still problematic and time consuming task when developing a model for practical situation. This paper presents a study where a parallel genetic algorithm (GA) is used for selecting the inputs and designing the high-level architecture of a multi-layer perceptron model for forecasting hourly concentrations of nitrogen dioxide at a busy urban traffic station in Helsinki. In addition, the tuning of GA's parameters for the problem is considered in experimental way. The results showed that the GA is a capable tool for tackling the practical problems of neural network design. However, it was observed that the evaluation of NN models is a computationally expensive process, which set limits for the search techniques. (C) 2004 Elsevier Ltd. All rights reserved.