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ANFIS Modeling of PMV Based on Hierarchical Fuzzy System

机译:基于分层模糊系统的PMV ANFIS建模

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The calculation of predicted mean vote (PMV) index is complex in real time when estimates indoor thermal comfort. As a result, some suitable model had been built to tackle this problem. In this paper, sensitivity analysis is used to sort the importance of each potential input variable on PMV. According to the results of ranking, the dimensional reduction and distribution of input space will be available. Then a T-S type hierarchical fuzzy system will be utilized to reflect PMV index by combining expert knowledge and the association analysis methods. After that the ANFIS is used to train and adjust the parameters of each subsystem through existing dataset. Simulation results show that it not only improves the accuracy but also reduce the total number of fuzzy rules.
机译:当估计室内热舒适性时,预测平均投票(PMV)指数的计算是复杂的。因此,建立了一些合适的模型来解决这个问题。在本文中,使用灵敏度分析来对PMV进行对每个潜在输入变量的重要性。根据排名结果,可用的输入空间的尺寸减少和分布。然后,将利用T-S型分层模糊系统通过组合专家知识和关联分析方法来反映PMV指数。之后,ANFI用于通过现有数据集培训和调整每个子系统的参数。仿真结果表明,它不仅提高了准确性,还可以减少模糊规则的总数。

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