Vacuum glass is widely used in many construction applications, including single-family homes, as a proven energy-saving method with outstanding heat preservation characteristics. The thermal insulation performance of vacuum glass is closely related to its heat transfer coefficient. In this study, we applied neural network methods to predict the heat transfer coefficients of vacuum glass. Using MATLAB, a neural network intelligence model was established, and the traditional back-propagation neural network (BPNN) was optimised. First, a genetic algorithm was used to reduce the dimensions of the independent variable. Then, the Mind Evolutionary Computation algorithm was used to optimise the initial weight and threshold. Using the optimised BPNN intelligence model to predict the heat transfer coefficient of vacuum glass insulation, we derived an average absolute error of 0.0076.

中文翻译：

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基于思维进化神经网络的真空玻璃保温传热系数智能建模预测

真空玻璃作为一种经过验证的节能方法，具有出色的保温特性，被广泛用于许多建筑应用，包括独户住宅。真空玻璃的隔热性能与其传热系数密切相关。在这项研究中，我们应用神经网络方法来预测真空玻璃的传热系数。利用MATLAB建立神经网络智能模型，对传统的反向传播神经网络（BPNN）进行优化。首先，使用遗传算法来减少自变量的维度。然后，使用Mind Evolutionary Computation算法优化初始权重和阈值。利用优化后的BPNN智能模型预测真空玻璃绝热传热系数，