Abstract
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.
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Li, C., Yi, J., Wang, M., Zhang, G. (2012). Prediction of Thermal Comfort Index Using Type-2 Fuzzy Neural Network. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_40
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DOI: https://doi.org/10.1007/978-3-642-31561-9_40
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