Prelims
Self-Learning and Adaptive Algorithms for Business Applications
ISBN: 978-1-83867-174-7, eISBN: 978-1-83867-171-6
Publication date: 21 June 2019
Citation
Hu, Z., Bodyanskiy, Y.V. and Tyshchenko, O.K. (2019), "Prelims", Self-Learning and Adaptive Algorithms for Business Applications, Emerald Publishing Limited, Leeds, pp. i-vii. https://doi.org/10.1108/978-1-83867-171-620191001
Publisher
:Emerald Publishing Limited
Copyright © 2019 Emerald Publishing Limited
Half Title Page
SELF-LEARNING AND ADAPTIVE ALGORITHMS FOR BUSINESS APPLICATIONS
Title Page
SELF-LEARNING AND ADAPTIVE ALGORITHMS FOR BUSINESS APPLICATIONS
A Guide to Adaptive Neuro-fuzzy Systems for Fuzzy Clustering under Uncertainty Conditions
BY
ZHENGBING HU
Central China Normal University, China
YEVGENIY V. BODYANSKIY
Kharkiv National University of Radio Electronics, Ukraine
OLEKSII K. TYSHCHENKO
University of Ostrava, Czech Republic
United Kingdom – North America – Japan – India – Malaysia – China
Copyright Page
Emerald Publishing Limited
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First edition 2019
Copyright © 2019 Emerald Publishing Limited
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British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-1-83867-174-7 (Print)
ISBN: 978-1-83867-171-6 (Online)
ISBN: 978-1-83867-173-0 (Epub)
Acknowledgment
The authors would like to thank their families and colleagues for comprehensive support.
Several people put much effort to make this book better.
The team of Emerald Publishing has done a great job of preparing the manuscript. They spent hours of their own time carefully reading through the material, finding typos, and making valuable substantive recommendations.
Each contributor spent many hours helping clarify the goals and concepts of the book, organize the material and generally make the text more intelligible and exciting.
Finally, we should also mention that this book was partially supported by the Research Association of Modern Education and Computer Science (RAMECS) and self-determined research funds of CCNU from the colleges’ primary research and operation of MOE (CCNU19TS022).