Self-learning and adaptive algorithms for business applications: a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions
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
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Bingley, UK
Emerald Publishing
2019
|
| Edition: | First edition |
| Series: | Emerald points
|
| Subjects: | |
| Online Access: | DE-92 DE-703 DE-824 DE-634 DE-1043 DE-M347 DE-863 DE-862 DE-523 DE-91 DE-473 DE-19 DE-355 DE-703 DE-20 DE-706 DE-824 DE-29 DE-739 DE-1046 Volltext |
| 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 |
| Physical Description: | 1 Online-Ressource (vii, 111 Seiten) |
| ISBN: | 9781838671716 1838671714 9781838671730 1838671730 |
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| 100 | 1 | |a Hu, Zhengbing |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Self-learning and adaptive algorithms for business applications |b a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |c by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko |
| 250 | |a First edition | ||
| 264 | 1 | |a Bingley, UK |b Emerald Publishing |c 2019 | |
| 300 | |a 1 Online-Ressource (vii, 111 Seiten) | ||
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| 337 | |b c |2 rdamedia | ||
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| 505 | 8 | |a Contents: 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; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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; 3.4.2.1. Confidence; 3.4.2.2. Diversification | |
| 505 | 8 | |a 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. 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 | |
| 505 | 8 | |a 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 | |
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| author | Hu, Zhengbing |
| author_facet | Hu, Zhengbing |
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| contents | Contents: 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; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. 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; 3.4.2.1. Confidence; 3.4.2.2. Diversification 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. 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 |
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| id | DE-604.BV046111829 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-20T07:17:20Z |
| institution | BVB |
| isbn | 9781838671716 1838671714 9781838671730 1838671730 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031492417 |
| oclc_num | 1119106405 |
| open_access_boolean | |
| owner | DE-634 DE-1043 DE-M347 DE-92 DE-863 DE-BY-FWS DE-862 DE-BY-FWS DE-523 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-20 DE-706 DE-824 DE-29 DE-739 DE-1046 |
| owner_facet | DE-634 DE-1043 DE-M347 DE-92 DE-863 DE-BY-FWS DE-862 DE-BY-FWS DE-523 DE-91 DE-BY-TUM DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-20 DE-706 DE-824 DE-29 DE-739 DE-1046 |
| physical | 1 Online-Ressource (vii, 111 Seiten) |
| psigel | ZDB-55-BME ZDB-1-EPB ZDB-55-BME19 ZDB-55-BME ZDB-55-BME19 ZDB-55-BME UER_Paketkauf_2019 |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Emerald Publishing |
| record_format | marc |
| series2 | Emerald points |
| spellingShingle | Hu, Zhengbing Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions Contents: 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; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. 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; 3.4.2.1. Confidence; 3.4.2.2. Diversification 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. 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 |
| title | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_auth | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_exact_search | Self-learning and adaptive algorithms for business applications a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | Self-learning and adaptive algorithms for business applications |
| title_sort | self learning and adaptive algorithms for business applications a guide to adaptive neuro fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_sub | a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions |
| url | https://www.emerald.com/insight/publication/doi/10.1108/9781838671716 |
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