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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Prediction of Thermal Comfort Index Using Type-2 Fuzzy Neural Network
verfasst von : Chengdong Li, Jianqiang Yi, Ming Wang, Guiqing Zhang
Erschienen in: Advances in Brain Inspired Cognitive Systems
Verlag: Springer Berlin Heidelberg
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Predicted Mean Vote (PMV) is the most widely-used index for evaluating the thermal comfort in buildings. But, this index is calculated through complicated iterations so that it is not suitable for real-time applications. To avoid complicated iterative calculation, this paper presents a prediction model for this index. The proposed model utilizes type-2 fuzzy neural network to approximate the input-output characteristic of the PMV model. To tune the parameters of this type-2 fuzzy neural prediction model, a hybrid algorithm which is a combination of the least square estimate (LSE) method and the back-propagation (BP) algorithm is provided. Finally, simulations are given to verify the effectiveness of the proposed prediction model.