Castillo Oscar, Castro Juan R, Pulido Martha, Melin Patricia
Tijuana Institute of Technology, Tijuana, Mexico.
UABC University, Tijuana, Mexico.
Eng Appl Artif Intell. 2022 Sep;114:105110. doi: 10.1016/j.engappai.2022.105110. Epub 2022 Jun 27.
In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Publicly available data sets of COVID-19 cases for several countries in the world were utilized to test the proposed approach. Simulation results of the COVID-19 data show the potential of the approach to outperform other aggregators in the literature.
在这项工作中，我们提出了一种用于神经网络集成预测的模糊聚合方法。聚合器用于集成中，以组合构成集成的网络的输出。这样做的目的是使集成的总输出优于各个模块的输出。在我们的方法中，使用模糊系统来估计在加权平均计算中组合输出时将分配给这些输出的权重。聚合过程中的不确定性用区间三型模糊进行建模，理论上它可以优于二型和一型模糊。利用世界上几个国家公开可用的新冠肺炎病例数据集来测试所提出的方法。新冠肺炎数据的模拟结果表明，该方法有潜力在性能上超过文献中的其他聚合器。
