Intelligent control in smart home based on adaptive neuro fuzzy inference system

Intelligent control in the smart house can be realized by analyzing the data in a sensor network and the user's previous behavior of operation to the household appliances, without the user's intervention. This control system can predict and control the household appliances intelligently to make whole household environment more environmentally friendly and comfortable. In order to improve the learning ability of home control system, to make full use of the sensor network data, this paper puts forward an adaptive neural fuzzy inference system (ANFIS) model based on K-means clustering method and improved particle swarm optimization algorithm. The model also went through the simulation of controlling the electric curtains of the smart house in the Matlab platform. Theoretical analysis and simulation experiments show that this model can improve the learning ability of home control system.