改进PSO-TSFNN智能家居室内空气质量检测与评价  

Home-automation indoor air quality testing and evaluating based on improved PSO-TSFNN

作　　者：陈双叶[1] 徐文政[1] 丁双春 咸耀山 Chen Shuangye Xu Wenzheng Ding Shuangchun Xian Yaoshan(College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China)

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《电子技术应用》2017年第1期84-87,91,共5页Application of Electronic Technique

基　　金：北京市科技计划课题重大科技成果转化落地培育项目(Z131100002413028)

摘　　要：针对室内空气质量评级存在多影响因子及随机变化的特点,在T-S模糊神经网络(TSFNN)基础上提出一种基于改进粒子群(PSO)优化的算法来对室内空气品质状况进行评价。根据GB/T18883-2002,选取室内代表性污染因子构建标准评价表;通过标准评价表对网络进行训练和测试,生成可用评价模型。结果表明,该模型能够对室内空气质量进行客观可靠的评价,为智能家居室内空气质量调控提供可靠保证,具有较高的实用价值。Evaluation of indoor air quality can be influenced by multi-factors and stochastic fluctuation. An improved T-S fuzzy neural network algorithm based on PSO is proposed to evalute the status of air quality in this paper. According to the GB/T18883- 2002, the indoor pollution factor representative will be selected and the standard evaluation form will be built. The network will be trained and tested by standard evaluation form and then available evaluation model is generated. The results show that the model has a great practical value in providing objective and reliable evaluation for indoor air quality, and guaranteeing for the smart home indoor air quality monitoring.

关 键 词：粒子群 T—S模糊神经网络 室内空气质量评价 智能家居 

分 类 号：TN711[电子电信—电路与系统]