Intelligent Home Systems for Ubiquitous User Support by Using Neural Networks and Rule-Based Approach

Abstract

Artificial intelligence methods applied in smart home environments can give a ubiquitous support of people, provide automatic control of system settings to lower the costs of operation, improve energetic efficiency, and longer endurance of components. In this article, we discuss the use of neural networks and rule-based systems as the components of automatic control over house elements. In the proposed framework, we store the knowledge and teach the system about the home in parallel to operation. Initially, the system starts with global settings, however, data are collected during use and parallel to this regular training is run so when the new knowledge guarantees higher efficiency, the system switch to use it. We have developed a new neural-based mechanism with rules control method to lower the costs of operation while keeping the needs of users. The components were tested and discussed due to practical application in our everyday life.