• 模糊神经网 - 引用次数：2

  Then samples are forecasted again using statistical model (second stage) based on the FNN model (first stage), some samples which are forecasted wrong at first stage are corrected at second stage, and the statistical model has little effect on samples which are forecasted right at first stage. Compared with the forecasting result of NN based on 40 dimensions from literature 5, the right ratio of sand-dust storm is improved from 60% to 73.3%, CSI is improved from 25.9% to 38.7% by the model combined FNN with statistical model pointed in this paper, the result is better than ever.

  于是，通过聚类建立非典型样本区，再构建基于非典型样本的统计模型，并设计出一种兼顾模糊神经网预报结果和样本非典型程度的沙尘暴隶属度调整方案，使建立在模糊神经网预报（1级）结果之上的统计模型再预报（2级），在基本不影响1级报对率的前提下，纠正了相当比例的报错样本，与文献5的基于40个特征的神经网络相比，本文提出的模糊神经网与统计模型的联合预报方案，使沙尘暴的报对率从60%提高到73.3%以上，CSI值也由25.9%提高到38.7%，预报效果得到明显改善。

  参考来源 - 基于模糊神经网的沙尘暴预报模型

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