使用神经分类器和天气预报对臭氧峰值和臭氧浓度水平的24小时预报
A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions
摘要：
一个神经网络结合到一个神经分类器中使用的实时预测的每小时最大臭氧在法国的中心，在城市大气中。该神经模型基于多层感知器（MLP）结构。统计网络的输入是来自法国国家气象局的天气预测的模型输出统计。使用这种神经分类器，预测的成功指数为78%，而经典的MLP为65%至72%。在2003年夏季的验证阶段，检测到了六个高于阈值的臭氧峰值。他们实际上是七个。
英文摘要：
A neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
