重庆理工大学学报（自然科学版）Issue(9)：102-107,6.DOI:10.3969/j.issn.1674-8425(z).2014.09.022
遗传神经网络在室内环境热舒适度融合评价中的应用研究
Research on Genetic Neural Network Fusion for Evaluation of Indoor Thermal Comfort Degree
摘要
Abstract
Aiming at the evaluation of indoor thermal comfort degree,and in order to solve the com-plex nonlinear relationship between the influencing factors of PMV(Predicted Mean Vote)index,the non-linear mapping approach of KPCA (kernel principal component analysis)is introduced to extract characteristics of input variables and to eliminate the nonlinear relationship between variables.Then based on GNN(genetic neural network),the fusion evaluation of indoor thermal comfort degree is im-plemented.By the comparison of GNN and KPCA+GNN,the simulative results show that:for the fu-sion evaluation of indoor thermal comfort degree,KPCA can extract the main influencing factors of PMV index,and KPCA+GNN is an effective forecasting approach with high accuracy.关键词
核主成分分析/遗传神经网络/热舒适度Key words
KPCA/genetic neural network/heat comfort degree分类
信息技术与安全科学引用本文复制引用
胡晓倩,张莲,蒋东荣..遗传神经网络在室内环境热舒适度融合评价中的应用研究[J].重庆理工大学学报（自然科学版）,2014,(9):102-107,6.基金项目
重庆市教委科技计划项目 （）
