Compensatory genetic fuzzy neural networks and their applications /:
This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore ; River Edge, N.J. :
World Scientific,
1997.
|
| Series: | Series in machine perception and artificial intelligence ;
vol. 30. |
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques. |
| Physical Description: | 1 online resource (xii, 186 pages) : illustrations |
| Bibliography: | Includes bibliographical references (pages 173-181) and index. |
| ISBN: | 9789812797674 981279767X |
Staff View
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| 505 | 0 | |a 1. Introduction. 1.1. Fuzzy sets and data granularity. 1.2. Neural networks and knowledge discovery. 1.3. Genetic algorithms and adaptive optimization. 1.4. Soft computing systems and computational intelligence. 1.5. Main issues -- 2. Fuzzy compensation principles. 2.1. Fuzzy yin-yang compensation. 2.2. Compensation of fuzzy CNF and fuzzy DNF. 2.3. 2-variable-2-dimensional CNFs and DNFs. 2.4. 2-variable-m-dimensional CNFs and DNFs for m = 3,4. 2.5. Compensation of universal fuzzy CNF and fuzzy DNF. 2.6. Summary -- 3. Normal fuzzy reasoning methodology. 3.1. Primary fuzzy subsets. 3.2. The variable-input-constant-output (VICO) problem. 3.3. Normal fuzzy reasoning (NFR). 3.4. Normal fuzzy controllers -- 4. Compensatory genetic fuzzy neural networks. 4.1. Introduction. 4.2. Fuzzy neural networks with knowledge discovery. 4.3. Heuristic genetic learning algorithm for a FNNKD. 4.4. Feature expressions of trapezoidal-type fuzzy sets. 4.5. Crisp-fuzzy neural networks (CFNN) -- 5. Fuzzy knowledge rediscovery in fuzzy rule bases. 5.1. Applicability of various defuzzification techniques. 5.2. Nonlinear function approximation -- 6. Fuzzy cart-pole balancing control systems. 6.1. Cart-pole balancing fuzzy control systems. 6.2. A cart-pole balancing system with crisp inputs and outputs. 6.3. A cart-pole balancing system with fuzzy inputs and outputs -- 7. Fuzzy knowledge compression and expansion. 7.1. Compression of fuzzy rule bases. 7.2. Expansion of fuzzy rule bases -- 8. Highly nonlinear system modeling and prediction. 8.1. Nonlinear function prediction. 8.2. Chaotic time series prediction. 8.3. Box and Jenkins's gas furnace model identification -- 9. Fuzzy moves in fuzzy games. 9.1. Introduction. 9.2. Fuzzy moves. 9.3. Normal fuzzy reasoning for fuzzy moves. 9.4. Applicability of various methods. 9.5. Efficient precise decision systems for fuzzy moves. 9.6. Typical examples. 9.7. Fuzzy moves in prisoner's dilemma. 9.8. Summary -- 10. Genetic neuro-fuzzy pattern recognition. 10.1. Structure of a genetic fuzzy neural network. 10.2. Genetic-algorithms-based self-organizing learning algorithm. 10.3. Simulations. 10.4. Conclusions -- 11. Constructive approach to modeling fuzzy systems. 11.1. Introduction. 11.2. A normal-fuzzy-reasoning-based fuzzy system. 11.3. Various single-input-single-output (SISO) fuzzy systems. 11.4. Universal approximation. 11.5. A piecewise nonlinear constructive algorithm. 11.6. Simulations. 11.7. Conclusions -- 12. Conclusions. 12.1. Main Conclusions. 12.2. Future research and development. | |
| 520 | |a This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques. | ||
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| contents | 1. Introduction. 1.1. Fuzzy sets and data granularity. 1.2. Neural networks and knowledge discovery. 1.3. Genetic algorithms and adaptive optimization. 1.4. Soft computing systems and computational intelligence. 1.5. Main issues -- 2. Fuzzy compensation principles. 2.1. Fuzzy yin-yang compensation. 2.2. Compensation of fuzzy CNF and fuzzy DNF. 2.3. 2-variable-2-dimensional CNFs and DNFs. 2.4. 2-variable-m-dimensional CNFs and DNFs for m = 3,4. 2.5. Compensation of universal fuzzy CNF and fuzzy DNF. 2.6. Summary -- 3. Normal fuzzy reasoning methodology. 3.1. Primary fuzzy subsets. 3.2. The variable-input-constant-output (VICO) problem. 3.3. Normal fuzzy reasoning (NFR). 3.4. Normal fuzzy controllers -- 4. Compensatory genetic fuzzy neural networks. 4.1. Introduction. 4.2. Fuzzy neural networks with knowledge discovery. 4.3. Heuristic genetic learning algorithm for a FNNKD. 4.4. Feature expressions of trapezoidal-type fuzzy sets. 4.5. Crisp-fuzzy neural networks (CFNN) -- 5. Fuzzy knowledge rediscovery in fuzzy rule bases. 5.1. Applicability of various defuzzification techniques. 5.2. Nonlinear function approximation -- 6. Fuzzy cart-pole balancing control systems. 6.1. Cart-pole balancing fuzzy control systems. 6.2. A cart-pole balancing system with crisp inputs and outputs. 6.3. A cart-pole balancing system with fuzzy inputs and outputs -- 7. Fuzzy knowledge compression and expansion. 7.1. Compression of fuzzy rule bases. 7.2. Expansion of fuzzy rule bases -- 8. Highly nonlinear system modeling and prediction. 8.1. Nonlinear function prediction. 8.2. Chaotic time series prediction. 8.3. Box and Jenkins's gas furnace model identification -- 9. Fuzzy moves in fuzzy games. 9.1. Introduction. 9.2. Fuzzy moves. 9.3. Normal fuzzy reasoning for fuzzy moves. 9.4. Applicability of various methods. 9.5. Efficient precise decision systems for fuzzy moves. 9.6. Typical examples. 9.7. Fuzzy moves in prisoner's dilemma. 9.8. Summary -- 10. Genetic neuro-fuzzy pattern recognition. 10.1. Structure of a genetic fuzzy neural network. 10.2. Genetic-algorithms-based self-organizing learning algorithm. 10.3. Simulations. 10.4. Conclusions -- 11. Constructive approach to modeling fuzzy systems. 11.1. Introduction. 11.2. A normal-fuzzy-reasoning-based fuzzy system. 11.3. Various single-input-single-output (SISO) fuzzy systems. 11.4. Universal approximation. 11.5. A piecewise nonlinear constructive algorithm. 11.6. Simulations. 11.7. Conclusions -- 12. Conclusions. 12.1. Main Conclusions. 12.2. Future research and development. |
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| discipline | Informatik |
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| id | ZDB-4-EBU-ocn824698679 |
| illustrated | Illustrated |
| indexdate | 2025-03-18T14:27:42Z |
| institution | BVB |
| isbn | 9789812797674 981279767X |
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| series | Series in machine perception and artificial intelligence ; |
| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Zhang, Yan-Qing. Compensatory genetic fuzzy neural networks and their applications / Yan-Qing Zhang, Abraham Kandel. Singapore ; River Edge, N.J. : World Scientific, 1997. 1 online resource (xii, 186 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Series in machine perception and artificial intelligence ; vol. 30 Includes bibliographical references (pages 173-181) and index. Print version record. 1. Introduction. 1.1. Fuzzy sets and data granularity. 1.2. Neural networks and knowledge discovery. 1.3. Genetic algorithms and adaptive optimization. 1.4. Soft computing systems and computational intelligence. 1.5. Main issues -- 2. Fuzzy compensation principles. 2.1. Fuzzy yin-yang compensation. 2.2. Compensation of fuzzy CNF and fuzzy DNF. 2.3. 2-variable-2-dimensional CNFs and DNFs. 2.4. 2-variable-m-dimensional CNFs and DNFs for m = 3,4. 2.5. Compensation of universal fuzzy CNF and fuzzy DNF. 2.6. Summary -- 3. Normal fuzzy reasoning methodology. 3.1. Primary fuzzy subsets. 3.2. The variable-input-constant-output (VICO) problem. 3.3. Normal fuzzy reasoning (NFR). 3.4. Normal fuzzy controllers -- 4. Compensatory genetic fuzzy neural networks. 4.1. Introduction. 4.2. Fuzzy neural networks with knowledge discovery. 4.3. Heuristic genetic learning algorithm for a FNNKD. 4.4. Feature expressions of trapezoidal-type fuzzy sets. 4.5. Crisp-fuzzy neural networks (CFNN) -- 5. Fuzzy knowledge rediscovery in fuzzy rule bases. 5.1. Applicability of various defuzzification techniques. 5.2. Nonlinear function approximation -- 6. Fuzzy cart-pole balancing control systems. 6.1. Cart-pole balancing fuzzy control systems. 6.2. A cart-pole balancing system with crisp inputs and outputs. 6.3. A cart-pole balancing system with fuzzy inputs and outputs -- 7. Fuzzy knowledge compression and expansion. 7.1. Compression of fuzzy rule bases. 7.2. Expansion of fuzzy rule bases -- 8. Highly nonlinear system modeling and prediction. 8.1. Nonlinear function prediction. 8.2. Chaotic time series prediction. 8.3. Box and Jenkins's gas furnace model identification -- 9. Fuzzy moves in fuzzy games. 9.1. Introduction. 9.2. Fuzzy moves. 9.3. Normal fuzzy reasoning for fuzzy moves. 9.4. Applicability of various methods. 9.5. Efficient precise decision systems for fuzzy moves. 9.6. Typical examples. 9.7. Fuzzy moves in prisoner's dilemma. 9.8. Summary -- 10. Genetic neuro-fuzzy pattern recognition. 10.1. Structure of a genetic fuzzy neural network. 10.2. Genetic-algorithms-based self-organizing learning algorithm. 10.3. Simulations. 10.4. Conclusions -- 11. Constructive approach to modeling fuzzy systems. 11.1. Introduction. 11.2. A normal-fuzzy-reasoning-based fuzzy system. 11.3. Various single-input-single-output (SISO) fuzzy systems. 11.4. Universal approximation. 11.5. A piecewise nonlinear constructive algorithm. 11.6. Simulations. 11.7. Conclusions -- 12. Conclusions. 12.1. Main Conclusions. 12.2. Future research and development. This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Fuzzy systems fast Neural networks (Computer science) fast Réseaux neuronaux (informatique) ram Systèmes flous. ram Kandel, Abraham. Print version: Zhang, Yan-Qing. Compensatory genetic fuzzy neural networks and their applications. Singapore ; River Edge, N.J. : World Scientific, 1997 9810233493 (DLC) 97044774 (OCoLC)37820143 Series in machine perception and artificial intelligence ; vol. 30. http://id.loc.gov/authorities/names/n91107585 |
| spellingShingle | Zhang, Yan-Qing Compensatory genetic fuzzy neural networks and their applications / Series in machine perception and artificial intelligence ; 1. Introduction. 1.1. Fuzzy sets and data granularity. 1.2. Neural networks and knowledge discovery. 1.3. Genetic algorithms and adaptive optimization. 1.4. Soft computing systems and computational intelligence. 1.5. Main issues -- 2. Fuzzy compensation principles. 2.1. Fuzzy yin-yang compensation. 2.2. Compensation of fuzzy CNF and fuzzy DNF. 2.3. 2-variable-2-dimensional CNFs and DNFs. 2.4. 2-variable-m-dimensional CNFs and DNFs for m = 3,4. 2.5. Compensation of universal fuzzy CNF and fuzzy DNF. 2.6. Summary -- 3. Normal fuzzy reasoning methodology. 3.1. Primary fuzzy subsets. 3.2. The variable-input-constant-output (VICO) problem. 3.3. Normal fuzzy reasoning (NFR). 3.4. Normal fuzzy controllers -- 4. Compensatory genetic fuzzy neural networks. 4.1. Introduction. 4.2. Fuzzy neural networks with knowledge discovery. 4.3. Heuristic genetic learning algorithm for a FNNKD. 4.4. Feature expressions of trapezoidal-type fuzzy sets. 4.5. Crisp-fuzzy neural networks (CFNN) -- 5. Fuzzy knowledge rediscovery in fuzzy rule bases. 5.1. Applicability of various defuzzification techniques. 5.2. Nonlinear function approximation -- 6. Fuzzy cart-pole balancing control systems. 6.1. Cart-pole balancing fuzzy control systems. 6.2. A cart-pole balancing system with crisp inputs and outputs. 6.3. A cart-pole balancing system with fuzzy inputs and outputs -- 7. Fuzzy knowledge compression and expansion. 7.1. Compression of fuzzy rule bases. 7.2. Expansion of fuzzy rule bases -- 8. Highly nonlinear system modeling and prediction. 8.1. Nonlinear function prediction. 8.2. Chaotic time series prediction. 8.3. Box and Jenkins's gas furnace model identification -- 9. Fuzzy moves in fuzzy games. 9.1. Introduction. 9.2. Fuzzy moves. 9.3. Normal fuzzy reasoning for fuzzy moves. 9.4. Applicability of various methods. 9.5. Efficient precise decision systems for fuzzy moves. 9.6. Typical examples. 9.7. Fuzzy moves in prisoner's dilemma. 9.8. Summary -- 10. Genetic neuro-fuzzy pattern recognition. 10.1. Structure of a genetic fuzzy neural network. 10.2. Genetic-algorithms-based self-organizing learning algorithm. 10.3. Simulations. 10.4. Conclusions -- 11. Constructive approach to modeling fuzzy systems. 11.1. Introduction. 11.2. A normal-fuzzy-reasoning-based fuzzy system. 11.3. Various single-input-single-output (SISO) fuzzy systems. 11.4. Universal approximation. 11.5. A piecewise nonlinear constructive algorithm. 11.6. Simulations. 11.7. Conclusions -- 12. Conclusions. 12.1. Main Conclusions. 12.2. Future research and development. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Fuzzy systems fast Neural networks (Computer science) fast Réseaux neuronaux (informatique) ram Systèmes flous. ram |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85052628 https://id.nlm.nih.gov/mesh/D016571 |
| title | Compensatory genetic fuzzy neural networks and their applications / |
| title_auth | Compensatory genetic fuzzy neural networks and their applications / |
| title_exact_search | Compensatory genetic fuzzy neural networks and their applications / |
| title_full | Compensatory genetic fuzzy neural networks and their applications / Yan-Qing Zhang, Abraham Kandel. |
| title_fullStr | Compensatory genetic fuzzy neural networks and their applications / Yan-Qing Zhang, Abraham Kandel. |
| title_full_unstemmed | Compensatory genetic fuzzy neural networks and their applications / Yan-Qing Zhang, Abraham Kandel. |
| title_short | Compensatory genetic fuzzy neural networks and their applications / |
| title_sort | compensatory genetic fuzzy neural networks and their applications |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Fuzzy systems fast Neural networks (Computer science) fast Réseaux neuronaux (informatique) ram Systèmes flous. ram |
| topic_facet | Neural networks (Computer science) Fuzzy systems. Neural Networks, Computer Réseaux neuronaux (Informatique) Systèmes flous. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Fuzzy systems Réseaux neuronaux (informatique) |
| work_keys_str_mv | AT zhangyanqing compensatorygeneticfuzzyneuralnetworksandtheirapplications AT kandelabraham compensatorygeneticfuzzyneuralnetworksandtheirapplications |