Abstract：
:Air pollution has emerged as an imminent issue in modern society. Prediction of pollutant levels is an important research topic in atmospheric environment today. For fulfilling such prediction, the use of neural network (NN), and in particular the multi-layer perceptrons, has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks, such as: 1) very slow convergence, and 2) easily getting stuck in a local minimum. In this paper, a newly developed method, particle swarm optimization (PSO) model, is adopted to train perceptrons, to predict pollutant levels, and as a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective by predicting some real air-quality problems.
journal_name
Environ Monit Assessjournal_title
Environmental monitoring and assessmentauthors
Lu WZ,Fan HY,Leung AY,Wong JCdoi
10.1023/a:1020274409612subject
Has Abstractpub_date
2002-11-01 00:00:00pages
217-30issue
3eissn
0167-6369issn
1573-2959journal_volume
79pub_type
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