The Extended Multidimensional Neo-Fuzzy System and Its Fast Learning in Pattern Recognition Tasks

Yevgeniy Bodyanskiy, Nonna Kulishova and Olha Chala
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Yevgeniy Bodyanskiy: Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, 61166 Kharkiv, Ukraine
Nonna Kulishova: Media Systems and Technologies Department, Kharkiv National University of Radioelectronics, 61166 Kharkiv, Ukraine
Olha Chala: Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, 61166 Kharkiv, Ukraine

Data, 2018, vol. 3, issue 4, 1-10

Abstract: Methods of machine learning and data mining are becoming the cornerstone in information technologies with real-time image and video recognition methods getting more and more attention. While computational system architectures are getting larger and more complex, their learning methods call for changes, as training datasets often reach tens and hundreds of thousands of samples, therefore increasing the learning time of such systems. It is possible to reduce computational costs by tuning the system structure to allow fast, high accuracy learning algorithms to be applied. This paper proposes a system based on extended multidimensional neo-fuzzy units and its learning algorithm designed for data streams processing tasks. The proposed learning algorithm, based on the information entropy criterion, has significantly improved the system approximating capabilities. Experiments have confirmed the efficiency of the proposed system in solving real-time video stream recognition tasks.

Keywords: extended multidimensional neo-fuzzy system; pattern recognition; entropy-information criterion; extended neo-fuzzy units (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
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