影响人体热舒适性的主要因素是温度和湿度,人体热舒适性又容易受人体个体差异的影响。利用HCMAC神经网络模型具有的结构简单、学习速度快、泛化能力强、易于硬件和软件实现等特点,在智能型热量计量与温湿度调节一体化装置中,根据用户对人居环境的不同温度和湿度的需求进行学习、联想记忆,实现了最佳温湿度匹配库的建立。运行结果表明,该方法学习速度快、精度高,可满足不同人的舒适性要求,大大提高人居环境的品质。 The main factors that affect the thermal comfort of the human body are temperature and humidity, and human thermal comfort is easily affected by individual differences in the human body. The HCMAC neural network model has the advantages of simple structure, fast learning speed, extensive generalization ability and easy realization of hardware and software. In the integrated device of intelligent calorimetry and temperature and humidity control, according to different temperature of living environment And humidity needs to learn, associative memory, to achieve the establishment of the best temperature and humidity matching library. The running results show that this method has the advantages of fast learning, high precision, satisfying the comfort requirements of different people and greatly improving the quality of living environment.