Statistical Evaluation of Four Technologies used for Intelectualization of Smart Home Environment
This paper addresses the issues of decision-making methods and their usage capabilities for intelligent control based on resident’s habits. Learning from the behaviour of the resident is essential for the system to adapt and provide intelligent control based on behaviour patterns. Different homes have different conditions and habits which have to be taken into account for the intelligent system to be useful. However, even deeply ingrained habits are subject to change over time. Therefore, an intelligent system has to respond to changing and diverse environment. Various decision-making methods have the potential of a number of benefits in providing intelligent control for the Smart home systems. In this paper, concurrent decision-making methods, including Artificial Neural Networks, Fuzzy Logic, Linear Programing and Bayesian, are employed with particular algorithms in order to provide control based on resident’s habits. These approaches are tested and compared within experimental scenarios for intelligent lightning control.