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49 results on '"Tyshchenko, Oleksii K."'

4. Comparative Analysis of Inductive Density Clustering Algorithms Meanshift and DBSCAN

Author

Hu, Zhengbing, Lurie, Irina, Tyshchenko, Oleksii K., Savina, Nataliia, Lytvynenko, Volodymyr, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Wang, Bo, editor, Petoukhov, Sergey, editor, and He, Matthew, editor

Published
2021
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5. The Possibilistic Gustafson-Kessel Fuzzy Clustering Procedure for Online Data Processing

Author

Hu, Zhengbing, Tyshchenko, Oleksii K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Dychka, Ivan, editor, and He, Matthew, editor

Published
2021
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6. Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Samitova, Viktoriia O.

Subjects
Computer Science - Learning
Abstract

A task of clustering data given in the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on memberhsip and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions., Comment: International Journal of Intelligent Systems and Applications(IJISA), Vol. 9, No. 2, February 2017

Published
2017
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7. Fuzzy Clustering Data Given in the Ordinal Scale

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Samitova, Viktoriia O.

Subjects
Computer Science - Artificial Intelligence
Abstract

A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.

Published
2017
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8. A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade

Author

Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Kopaliani, Daria S.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.

Published
2016

9. An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

Author

Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Kopaliani, Daria S.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

Published
2016

10. An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Boiko, Olena O.

Subjects
Computer Science - Artificial Intelligence
Abstract

A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.

Published
2016

11. Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Boiko, Olena O.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.

Published
2016

12. An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Boiko, Olena O.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.

Published
2016

13. An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

Author

Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Deineko, Anastasiia O.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

An architecture of a new neuro-fuzzy system is proposed. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results prove the effectiveness of the developed architecture and the learning procedure.

Published
2016

15. An Approach to Online Fuzzy Clustering Based on the Mahalanobis Distance Measure

Author

Hu, Zhengbing, Tyshchenko, Oleksii K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, and He, Matthew, editor

Published
2020
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16. A Hybrid Cascade Neural Network with Ensembles of Extended Neo-Fuzzy Neurons and Its Deep Learning

Author

Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Kulczycki, Piotr, editor, Mesiar, Radko, editor, and Wisniewski, Rafal, editor

Published
2020
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17. Self-learning Procedures for a Kernel Fuzzy Clustering System

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy, Tyshchenko, Oleksii K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Hu, Zhengbing, editor, Petoukhov, Sergey, editor, Dychka, Ivan, editor, and He, Matthew, editor

Published
2019
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18. Fuzzy Clustering High-Dimensional Data Using Information Weighting

Author

Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., Mashtalir, Sergii V., Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2019
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20. A Multidimensional Adaptive Growing Neuro-Fuzzy System and Its Online Learning Procedure

Author

Hu, Zhengbing, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Shakhovska, Natalia, editor, and Stepashko, Volodymyr, editor

Published
2018
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21. A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

Author

Bodyanskiy Yevgeniy V. and Tyshchenko Oleksii K.

Subjects
data stream, membership function, training procedure, adaptive neuro-fuzzy system, extended neo-fuzzy neuron, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.

Published
2019
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25. Introduction

Author

Hu, Zhengbing, primary, Bodyanskiy, Yevgeniy V., additional, and Tyshchenko, Oleksii K., additional

Published
2019
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28. Conclusion

Author

Hu, Zhengbing, primary, Bodyanskiy, Yevgeniy V., additional, and Tyshchenko, Oleksii K., additional

Published
2019
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44. A HYBRID CASCADE NEURO--FUZZY NETWORKWITH POOLS OF EXTENDED NEO--FUZZY NEURONS AND ITS DEEP LEARNING.

Author

BODYANSKIY, YEVGENIY V. and TYSHCHENKO, OLEKSII K.

Subjects
DEEP learning, NEURONS, CASCADE connections, ONLINE education, MEMBERSHIP functions (Fuzzy logic)
Abstract

This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron. [ABSTRACT FROM AUTHOR]

Published
2019
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48. FUZZY DATA CLUSTERING IN THE RANK SCALE BASED ON A DOUBLE NEO-FUZZY NEURON.

Author

Zhengbing Hu, Bodyanskiy, Yevgeniy V., Tyshchenko, Oleksii K., and Samitova, Viktoriia O.

Subjects
FUZZY clustering technique, INFORMATION processing, MEMBERSHIP functions (Fuzzy logic)
Abstract

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2017
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