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This paper examines existing multi-criteria decision-making methods, in particular, for solving the problem of ranking (evaluating) political parties. The use of expert evaluations plays an important role at the present stage of decision-making theory development, which is connected with the increasing frequency of their use in the practice of modern political decisions. It is revealed that some methods have certain limitations, in particular, processing only accurate data (expert assessments), impossibility to take into account fuzzy (interval) assessments, lack of group expert evaluation, complexity of forming expert judgments, necessity to take into account the consistency of judgments. The authors propose the use of the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) method that is devoid of appropriate limitations and is effective for use in solving the problem of ranking political parties. In this method, the alternative closest to the Fuzzy Positive Ideal Solution (FPIS) and farthest from the Fuzzy Negative Ideal Solution (FNIS) is considered optimal. Herewith the FPIS consists of the best values of each alternative, while the FNIS consists of the worst values.

In this paper, methods for noise filtration in the image recognition processes are considered. The following noise filtration methods were analyzed: arithmetic averaging filter, geometric averaging filter, median filtering, adaptive median filtration, Gaussian filtration and filtration using soft computing techniques, in particular the fuzzy color preserving Gaussian noise reduction method (FCG filter). Besides, the different types of noise that may occur on a digital image are discussed. All methods were evaluated using metrics like mean squared error (MSE), peak signal-to-noise ratio (PSNR) and structure similarity of images (SSIM). It has been found that all of the above methods can well filter out only a certain type of noise. In this paper, a combination of adaptive median filtering and FGG filter is proposed for removal of combined pulse and Gaussian noises

This paper discusses the design of modern tactile and slip displacement sensors for information-control systems of intelligent and adaptive robots. It provides information on three approaches for using slip displacement signals (for correction of claiming force, for identification of manipulated object mass and for correction of robot control algorithm). The study presents the analysis of different methods for slip displacement signals detection, as well as new sensors schemes, mathematical models and correction methods. Special attention is paid to investigations of sensors developed by authors with the capacity, magnetic sensitive elements and with automatic adjustment of clamping force. The proposed structure of multi-functional computerized information-control system for intelligent robot is presented.

This paper discusses the design of modern tactile sensor systems for intelligent and adaptive robots. It provides information on three approaches for using slip displacement signals (for correction of claiming force, for identification of manipulated object mass and for correction of robot control algorithm). The study presents the analysis of different methods for slip displacement signals detection, as well as new sensors schemes, mathematical models and correction methods. Special attention is paid to investigations of developed by authors sensors with the capacity, magnetic sensitive elements and with automatic adjustment of claiming force.