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A fuzzy neural network model for predicting clothing thermal comfort

机译:服装热舒适性的模糊神经网络模型

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This paper presents a Fuzzy Neural Network (FNN) based local to overall thermal sensation model for prediction of clothing thermal function in functional textile design system. Unlike previous experimental and regression analysis approaches, this model depends on direct factors of human thermal response — body core and skin temperatures. First the local sensation is predicted by a FNN network using local body part skin temperatures, their change rates, and core temperature as inputs; then the overall sensation is predicted. This is also performed by a FNN network. The FNN networks are developed on the basis of the Feed-Forward Back-Propagation (FFBP) network; the advantage of using fuzzy logic here is to reduce the requirement of training data. The simulation result shows a good correlation between predicted and the traditional experimental data.
机译:本文提出了一种基于局部到整体的热感模型的模糊神经网络(FNN),用于预测功能纺织品设计系统中服装的热功能。与以前的实验和回归分析方法不同,此模型取决于人类热响应的直接因素-身体核心和皮肤温度。首先,通过FNN网络使用局部身体部位的皮肤温度,其变化率和核心温度作为输入来预测局部感觉;然后预测整体感觉。这也由FNN网络执行。 FNN网络是基于前馈反向传播(FFBP)网络开发的;在这里使用模糊逻辑的优势是减少训练数据的需求。仿真结果表明预测数据与传统实验数据具有良好的相关性。

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