Abstract-An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN's adaptive learning algorithm allows solving classification problems in a real-time fashion.

Many tasks require human facial expressions automatic recognition in real time. Recent solutions to this problem using machine learning methods have been based on the applying of training data sets that include hundreds of thousands of samples. The formation of these data is too costly. In this paper, the architecture of a system using extended neofuzzy neurons for online emotions recognition is examined. We propose the algorithm which is based on the entropy criterion for learning the system and reducing the amount of training data thousands of times.

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The problem of interpolation of a two-dimensional function on a nonuniform axial rectangular grid is considered. To solve the problem, a memory-based neuro-fuzzy system is proposed. This system is computationally simple and provides a high-quality interpolation.