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ANFIS Modeling of PMV Based on Hierarchical Fuzzy System

机译:基于层次模糊系统的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:当估算室内热舒适度时,预测平均投票(PMV)指数的计算实时复杂。结果,已经建立了一些合适的模型来解决这个问题。在本文中,灵敏度分析用于对PMV上每个潜在输入变量的重要性进行排序。根据排序的结果,可以减小输入空间的尺寸并进行分配。然后,结合专家知识和关联分析方法,利用T-S型分层模糊系统来反映PMV指标。之后,ANFIS用于通过现有数据集训练和调整每个子系统的参数。仿真结果表明,该算法不仅提高了精度,而且减少了模糊规则的总数。

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