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Author Hu, Zhengbing, author.

Title Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko.

Publication Info. Bingley, UK : Emerald Publishing, 2019.

Item Status

Edition First edition.
Description 1 online resource.
Physical Medium polychrome
Description text file
Series Emerald points
Emerald points.
Bibliography Includes bibliographical references.
Contents Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; Interval Type-2 Fuzzy Sets; Model Reduction; Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning
1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method
2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; Confidence; Diversification Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; An Algebraic Combination; A Weighted Combination; Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter
4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References
Summary 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.
Local Note eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America
Subject Electronic data processing.
Electronic data processing.
Business -- Data processing.
Business -- Data processing.
Fuzzy systems.
Fuzzy systems.
Genre/Form Electronic books.
Electronic books.
Added Author Bodyanskiy, Yevgeniy V., author.
Tyshchenko, Oleksii, author.
Other Form: Print version : 9781838671747
ISBN 9781838671716 (electronic book)
1838671714 (electronic book)
9781838671730 (ePub ebook)