Presented at the Ist International Symposium on Innovative Approaches in Scientific Studies (ISAS 2018), Kemer-Antalya, Turkey, Apr 11, 2018

SETSCI Conference Proceedings, 2018, 2, Page (s): 140-141 , https://doi.org/

Published Date: 23 June 2018    | 1096     9

Many situations in human life are described with uncertain narratives. The fuzzy set theory has revealed as a powerful tool to deal with the uncertainty of human thoughts and expressions, by Azerbaijani Prof. Dr. Lotfi A. Zadeh in 1965. In the classical cluster method, an element is either included or not included in the cluster. In the fuzzy set method, the element can take a value between 0 and 1. In the classical cluster method, while an environment is specified as definite expressions such as hot or cold, with the help of the fuzzy set method, it was possible to make imprecise mathematical models such as little hot, very hot, little cold, very cold. For example, in a classical cluster method, if a population with a height greater than 1.80m  and 1.80m is tall and a person shorter than 1.80m is short, a person with a height of 1.75m is considered short. At the same time, a person with a height of 1.55 is regarded as short. However, there is a serious difference between these two people. In the fuzzy logic theorem, a person with a height of 1.75m is little short and a person with a length of 1.55 is very short.

Fuzzy control, which is based on the knowledge and experience of a person and transformed into rules and mathematical functions, is necessary to control nonlinear systems and to improve the performance of developed applications. This control technology is developing rapidly, since it is preferred in a wide range of products, from products we use in our homes to industrial appliances. Nowadays, living areas are being designed making human life even easier and made more comfortable every day. One of the most suitable control methods for home automation is fuzzy control, due to the non-linearity and complex processing of external data.

Systems based on fuzzy logic, which allows for a flexible model, seems to be more featured in smart home systems in the following years, with the help of microprocessors, with an approach that is close to the human thought system. In addition to the ambient temperature and humidity values in the home being at the desired value, the amount of light and the vibration of the light is also an important influence on people's health in the lighting systems. A bad lighting system causes headaches, eye disturbances and an irritable mood. In this study, the importance of developing a user friendly system by controlling the temperature, humidity and illumination in the living area from the physiological point of view is discussed and advantages of using fuzzy modeling technique for control systems in closed spaces, with the fuzzy control model software embedded in the microcontroller.

Developed applications with fuzzy control base, view of different field emerges each day. By embedding software into microcontrollers developed with fuzzy logic using appropriate membership functions, without human intervention, as if there were human beings, that real-time systems imitating humanity with artificial intelligence applications are the benefits that energy saving provides and it is seen that it gives successful results with lower costs. Fuzzy control has many advantages over conventional control methods in the realized system. For example, in conventional control systems, if the air is cold, the heater is operated. However, in the fuzzy control technique, it is first determined how cold the air is. According to the results obtained, the temperature of the heater is adjusted to the required amount. For the desired humidity and temperature values, more accurate, faster and more stable results are obtained with fuzzy control than the conventional control method. Even under different ambient conditions, it seems that the system has set the optimum values for people and worked according to their needs.
 

Keywords - rtificial intelligence, Fuzzy control, Physiological home automation, Fuzzy logic with microcontrollers