Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions

Zhengbing Hu
Central China Normal University, China

Yevgeniy V. Bodyanskiy
Kharkiv National University of Radio Electronics, Ukraine

Oleksii Tyshchenko
University of Ostrava, Czech Republic

Product Details
25 Jun 2019
Emerald Publishing Limited
120 pages - 129 x 198mm
Emerald Points


In today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications. 
In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning.
1. Review of the Problem Area 
2. Adaptive Methods of Fuzzy Clustering 
3. Kohonen Maps and their Ensembles for Fuzzy Clustering Tasks 
4. Simulation Results and Solutions for Practical Tasks 
Zhengbing Hu is an Associate Professor, School of Educational Information Technology, Huazhong Normal University, China. 
Yevgeniy V. Bodyanskiy is a Professor at the Department of Artificial Intelligence, Kharkiv National University of Radioelectronics. 
Oleksii Tyshchenko is a Researcher at the Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic.

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