In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

The style differentiation is considered as an interrelation of phonostatistical structures of the researched style system on the phonological level. A new approach to this problem consists of determination by the hypothesis, ranking and style distance determination methods a high, medium and low level of difference between the paired styles. The results obtained show a strong, medium and weak effect of style factor. The established essential differences are caused by the style factor effect. The established unessential differences are caused by the language factor effect. The model proposed shows the interrelation of style and language factors. The statistical methods applied allow to identify with mathematical precision stylistic peculiarities of a text and relate it to a particular class of style, historical period, literary trend as well as establish its authorship.

In the work, a software model based on the graph-based SLAM methodology is developed using the LIDAR technology, which allows direct and automatic control of the mobile robot in real-time. The architecture of the developed software and the main algorithms on which the work of the system is based is described. The technical support provided by the Arduino microcontroller is developed. Examples of application of built-in software are given.

In the article developed the structure of the automated system for house devices control using machine learning algorithms. The main element of the proposed structure is responsible for setting up automated devices parameters, according to the data from sensors in the home, based on decision making using artificial neural network.