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A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions

By: Oleksii Tyshchenko, Zhengbing Hu, Yevgeniy V. Bodyanskiy

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Paperback | 25 June 2019 | Edition Number 1

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Paperback
120 Pages

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20 x 12.5 x 1

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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.

Industry Reviews

This guide explains how to apply methods using systems built by a combination of the neural network approach and fuzzy logic (neuro-fuzzy systems) to solve practical data classification problems in business. It describes methods aimed at handling the main types of uncertainties in data, using adaptive methods of fuzzy clustering; the use of Kohonen maps and their ensembles for fuzzy clustering tasks; and simulation results of these neuro-fuzzy architectures, their learning methods, self-organization, and clustering procedures.--Annotation (c)2019 "(protoview.com)"

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