Soft computing and its applications:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New Jersey
World Scientific
© 2001
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| Subjects: | |
| Online Access: | TUM01 |
| Physical Description: | 1 Online-Ressource |
| ISBN: | 9789812799845 9812799842 |
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| 505 | 8 | |a Intro; Contents; Foreword; Preface; 1. Aims of Soft Computing; 1.1. Soft Computing (SC) as Key Methodology for Designing of Intelligent Systems; 1.2. Strtucture and Constituents of Soft Computing; 1.3. Comparative Characteristics of the Constituents of SC; 1.4. Intelligent Combinations of the Components of SC; 1.5. References; 2. Foundations of Fuzzy Sets Theory; 2.1. Basic Concepts and Properties of Fuzzy Sets; 2.1.1. Basic types of fuzzy sets; 2.1.2. Properties of fuzzy sets; 2.1.3. Representations and constructing of fuzzy sets; 2.1.4. Extension principle for fuzzy sets | |
| 505 | 8 | |a 2.1.5. Operations on fuzzy sets2.1.6. Probability and fuzziness; 2.1.7. Fuzzy sets and possibility theory; 2.2. Elements of Fuzzy Mathematics; 2.2.1. Fuzzy topology and fuzzy algebra; 2.2.2. Fuzzy geometry; 2.2.3. Fuzzy arithmetic; 2.2.4. Fuzzy analysis; 2.2.5. Fuzzy differential equations; 2.3. Fuzzy Relations; 2.3.1. Fuzzy relations, linguistic variables; 2.3.2. Fuzzy relational equations; 2.4. References; 3. Fuzzy Logic; 3.1. Brief Overview of Classical Logic; 3.2. Elements of Fuzzy Logic; 3.3. Semantic Analysis of Different Fuzzy Logics | |
| 505 | 8 | |a 3.4. Fuzzy Inference Rules and Approximate Reasoning3.5. Formalization of the Fuzzy Conditional Inference for a Different Type of Conditional Propositions; 3.6. Experimental Selecting Appropriate Fuzzy Implications; 3.7. References; 4. Applications of Fuzzy Sets; 4.1. Fuzzy Modeling; 4.2. Fuzzy Decision Making; 4.3. Pattern Analysis and Classification; 4.4. Fuzzy Control Systems; 4.5. Fuzzy Information Processing; 4.5.1. Fuzzy information systems; 4.5.2. Fuzzy data base and fuzzy operations in fuzzy data base management systems; 4.5.3. Fuzzy information retrieval; 4.6. Fuzzy Robotics | |
| 505 | 8 | |a 4.6.1. Fuzzy control system for robot4.6.2. Optimal path planning for a mobile intelligence robot with fuzzy resolution principle; 4.7. References; 5. Artificial Neural Networks; 5.1. Models of Neuron. Basic Architectures of Neural Networks; 5.2. Feed-Forward Neural Networks; 5.3. Recurrent Neural Networks; 5.4. Time-Delay Neural Networks; 5.5. Radial Basis Function Neural Networks; 5.6. Cerebellar Model Articulation Controller; 5.7. Adaptive Resonance Theory (ART) NN; 5.8. Associative Neural Memory Models; 5.9. Supervised Learning of Neural Networks; 5.9.1. Perceptron learning algorithm | |
| 505 | 8 | |a 5.9.2. Widrow-Hoff learning rule5.9.3 Delta rule; 5.9.4. Error backpropagation algorithm; 5.9.5. Recurrent backpropagation; 5.10. Unsupervised Learning; 5.10.1. Hebbian learning; 5.10.2. Competitive learning; 5.10.3. Kohonen's learning; 5.11. Reinforcement Learning; 5.12. Application of ANN; 5.12.1. Neural identification and control; 5.12.2. Adaptive predictive neural control system for industrial robot; 5.13. References; 6. Probabilistic Reasoning; 6.1. Bayesian Approach; 6.2. Dempster-Shafer Theory of Belief; 6.3. Upper and Lower Prevision; 6.4. Mixed Formalism Based Reasoning | |
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| author | Aliev, R. A., (Rafik Aziz ogly) |
| author_facet | Aliev, R. A., (Rafik Aziz ogly) |
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| contents | Intro; Contents; Foreword; Preface; 1. Aims of Soft Computing; 1.1. Soft Computing (SC) as Key Methodology for Designing of Intelligent Systems; 1.2. Strtucture and Constituents of Soft Computing; 1.3. Comparative Characteristics of the Constituents of SC; 1.4. Intelligent Combinations of the Components of SC; 1.5. References; 2. Foundations of Fuzzy Sets Theory; 2.1. Basic Concepts and Properties of Fuzzy Sets; 2.1.1. Basic types of fuzzy sets; 2.1.2. Properties of fuzzy sets; 2.1.3. Representations and constructing of fuzzy sets; 2.1.4. Extension principle for fuzzy sets 2.1.5. Operations on fuzzy sets2.1.6. Probability and fuzziness; 2.1.7. Fuzzy sets and possibility theory; 2.2. Elements of Fuzzy Mathematics; 2.2.1. Fuzzy topology and fuzzy algebra; 2.2.2. Fuzzy geometry; 2.2.3. Fuzzy arithmetic; 2.2.4. Fuzzy analysis; 2.2.5. Fuzzy differential equations; 2.3. Fuzzy Relations; 2.3.1. Fuzzy relations, linguistic variables; 2.3.2. Fuzzy relational equations; 2.4. References; 3. Fuzzy Logic; 3.1. Brief Overview of Classical Logic; 3.2. Elements of Fuzzy Logic; 3.3. Semantic Analysis of Different Fuzzy Logics 3.4. Fuzzy Inference Rules and Approximate Reasoning3.5. Formalization of the Fuzzy Conditional Inference for a Different Type of Conditional Propositions; 3.6. Experimental Selecting Appropriate Fuzzy Implications; 3.7. References; 4. Applications of Fuzzy Sets; 4.1. Fuzzy Modeling; 4.2. Fuzzy Decision Making; 4.3. Pattern Analysis and Classification; 4.4. Fuzzy Control Systems; 4.5. Fuzzy Information Processing; 4.5.1. Fuzzy information systems; 4.5.2. Fuzzy data base and fuzzy operations in fuzzy data base management systems; 4.5.3. Fuzzy information retrieval; 4.6. Fuzzy Robotics 4.6.1. Fuzzy control system for robot4.6.2. Optimal path planning for a mobile intelligence robot with fuzzy resolution principle; 4.7. References; 5. Artificial Neural Networks; 5.1. Models of Neuron. Basic Architectures of Neural Networks; 5.2. Feed-Forward Neural Networks; 5.3. Recurrent Neural Networks; 5.4. Time-Delay Neural Networks; 5.5. Radial Basis Function Neural Networks; 5.6. Cerebellar Model Articulation Controller; 5.7. Adaptive Resonance Theory (ART) NN; 5.8. Associative Neural Memory Models; 5.9. Supervised Learning of Neural Networks; 5.9.1. Perceptron learning algorithm 5.9.2. Widrow-Hoff learning rule5.9.3 Delta rule; 5.9.4. Error backpropagation algorithm; 5.9.5. Recurrent backpropagation; 5.10. Unsupervised Learning; 5.10.1. Hebbian learning; 5.10.2. Competitive learning; 5.10.3. Kohonen's learning; 5.11. Reinforcement Learning; 5.12. Application of ANN; 5.12.1. Neural identification and control; 5.12.2. Adaptive predictive neural control system for industrial robot; 5.13. References; 6. Probabilistic Reasoning; 6.1. Bayesian Approach; 6.2. Dempster-Shafer Theory of Belief; 6.3. Upper and Lower Prevision; 6.4. Mixed Formalism Based Reasoning |
| ctrlnum | (OCoLC)1150863769 (DE-599)BVBBV046636958 |
| discipline | Informatik |
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| format | Electronic eBook |
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| id | DE-604.BV046636958 |
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| index_date | 2024-07-03T14:13:14Z |
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| isbn | 9789812799845 9812799842 |
| language | English |
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| spelling | Aliev, R. A., (Rafik Aziz ogly) Verfasser aut Soft computing and its applications R.A. Aliev, R.R. Aliev New Jersey World Scientific © 2001 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Intro; Contents; Foreword; Preface; 1. Aims of Soft Computing; 1.1. Soft Computing (SC) as Key Methodology for Designing of Intelligent Systems; 1.2. Strtucture and Constituents of Soft Computing; 1.3. Comparative Characteristics of the Constituents of SC; 1.4. Intelligent Combinations of the Components of SC; 1.5. References; 2. Foundations of Fuzzy Sets Theory; 2.1. Basic Concepts and Properties of Fuzzy Sets; 2.1.1. Basic types of fuzzy sets; 2.1.2. Properties of fuzzy sets; 2.1.3. Representations and constructing of fuzzy sets; 2.1.4. Extension principle for fuzzy sets 2.1.5. Operations on fuzzy sets2.1.6. Probability and fuzziness; 2.1.7. Fuzzy sets and possibility theory; 2.2. Elements of Fuzzy Mathematics; 2.2.1. Fuzzy topology and fuzzy algebra; 2.2.2. Fuzzy geometry; 2.2.3. Fuzzy arithmetic; 2.2.4. Fuzzy analysis; 2.2.5. Fuzzy differential equations; 2.3. Fuzzy Relations; 2.3.1. Fuzzy relations, linguistic variables; 2.3.2. Fuzzy relational equations; 2.4. References; 3. Fuzzy Logic; 3.1. Brief Overview of Classical Logic; 3.2. Elements of Fuzzy Logic; 3.3. Semantic Analysis of Different Fuzzy Logics 3.4. Fuzzy Inference Rules and Approximate Reasoning3.5. Formalization of the Fuzzy Conditional Inference for a Different Type of Conditional Propositions; 3.6. Experimental Selecting Appropriate Fuzzy Implications; 3.7. References; 4. Applications of Fuzzy Sets; 4.1. Fuzzy Modeling; 4.2. Fuzzy Decision Making; 4.3. Pattern Analysis and Classification; 4.4. Fuzzy Control Systems; 4.5. Fuzzy Information Processing; 4.5.1. Fuzzy information systems; 4.5.2. Fuzzy data base and fuzzy operations in fuzzy data base management systems; 4.5.3. Fuzzy information retrieval; 4.6. Fuzzy Robotics 4.6.1. Fuzzy control system for robot4.6.2. Optimal path planning for a mobile intelligence robot with fuzzy resolution principle; 4.7. References; 5. Artificial Neural Networks; 5.1. Models of Neuron. Basic Architectures of Neural Networks; 5.2. Feed-Forward Neural Networks; 5.3. Recurrent Neural Networks; 5.4. Time-Delay Neural Networks; 5.5. Radial Basis Function Neural Networks; 5.6. Cerebellar Model Articulation Controller; 5.7. Adaptive Resonance Theory (ART) NN; 5.8. Associative Neural Memory Models; 5.9. Supervised Learning of Neural Networks; 5.9.1. Perceptron learning algorithm 5.9.2. Widrow-Hoff learning rule5.9.3 Delta rule; 5.9.4. Error backpropagation algorithm; 5.9.5. Recurrent backpropagation; 5.10. Unsupervised Learning; 5.10.1. Hebbian learning; 5.10.2. Competitive learning; 5.10.3. Kohonen's learning; 5.11. Reinforcement Learning; 5.12. Application of ANN; 5.12.1. Neural identification and control; 5.12.2. Adaptive predictive neural control system for industrial robot; 5.13. References; 6. Probabilistic Reasoning; 6.1. Bayesian Approach; 6.2. Dempster-Shafer Theory of Belief; 6.3. Upper and Lower Prevision; 6.4. Mixed Formalism Based Reasoning Soft Computing (DE-588)4455833-8 gnd rswk-swf Electronic books Soft Computing (DE-588)4455833-8 s DE-604 Aliev, R. R. Sonstige oth Erscheint auch als Aliev, R.A. (Rafik Aziz ogly) Soft computing and its applications Singapore ; New Jersey : World Scientific, ©2001 Druck-Ausgabe 9810247001 978-981-02-4700-3 |
| spellingShingle | Aliev, R. A., (Rafik Aziz ogly) Soft computing and its applications Intro; Contents; Foreword; Preface; 1. Aims of Soft Computing; 1.1. Soft Computing (SC) as Key Methodology for Designing of Intelligent Systems; 1.2. Strtucture and Constituents of Soft Computing; 1.3. Comparative Characteristics of the Constituents of SC; 1.4. Intelligent Combinations of the Components of SC; 1.5. References; 2. Foundations of Fuzzy Sets Theory; 2.1. Basic Concepts and Properties of Fuzzy Sets; 2.1.1. Basic types of fuzzy sets; 2.1.2. Properties of fuzzy sets; 2.1.3. Representations and constructing of fuzzy sets; 2.1.4. Extension principle for fuzzy sets 2.1.5. Operations on fuzzy sets2.1.6. Probability and fuzziness; 2.1.7. Fuzzy sets and possibility theory; 2.2. Elements of Fuzzy Mathematics; 2.2.1. Fuzzy topology and fuzzy algebra; 2.2.2. Fuzzy geometry; 2.2.3. Fuzzy arithmetic; 2.2.4. Fuzzy analysis; 2.2.5. Fuzzy differential equations; 2.3. Fuzzy Relations; 2.3.1. Fuzzy relations, linguistic variables; 2.3.2. Fuzzy relational equations; 2.4. References; 3. Fuzzy Logic; 3.1. Brief Overview of Classical Logic; 3.2. Elements of Fuzzy Logic; 3.3. Semantic Analysis of Different Fuzzy Logics 3.4. Fuzzy Inference Rules and Approximate Reasoning3.5. Formalization of the Fuzzy Conditional Inference for a Different Type of Conditional Propositions; 3.6. Experimental Selecting Appropriate Fuzzy Implications; 3.7. References; 4. Applications of Fuzzy Sets; 4.1. Fuzzy Modeling; 4.2. Fuzzy Decision Making; 4.3. Pattern Analysis and Classification; 4.4. Fuzzy Control Systems; 4.5. Fuzzy Information Processing; 4.5.1. Fuzzy information systems; 4.5.2. Fuzzy data base and fuzzy operations in fuzzy data base management systems; 4.5.3. Fuzzy information retrieval; 4.6. Fuzzy Robotics 4.6.1. Fuzzy control system for robot4.6.2. Optimal path planning for a mobile intelligence robot with fuzzy resolution principle; 4.7. References; 5. Artificial Neural Networks; 5.1. Models of Neuron. Basic Architectures of Neural Networks; 5.2. Feed-Forward Neural Networks; 5.3. Recurrent Neural Networks; 5.4. Time-Delay Neural Networks; 5.5. Radial Basis Function Neural Networks; 5.6. Cerebellar Model Articulation Controller; 5.7. Adaptive Resonance Theory (ART) NN; 5.8. Associative Neural Memory Models; 5.9. Supervised Learning of Neural Networks; 5.9.1. Perceptron learning algorithm 5.9.2. Widrow-Hoff learning rule5.9.3 Delta rule; 5.9.4. Error backpropagation algorithm; 5.9.5. Recurrent backpropagation; 5.10. Unsupervised Learning; 5.10.1. Hebbian learning; 5.10.2. Competitive learning; 5.10.3. Kohonen's learning; 5.11. Reinforcement Learning; 5.12. Application of ANN; 5.12.1. Neural identification and control; 5.12.2. Adaptive predictive neural control system for industrial robot; 5.13. References; 6. Probabilistic Reasoning; 6.1. Bayesian Approach; 6.2. Dempster-Shafer Theory of Belief; 6.3. Upper and Lower Prevision; 6.4. Mixed Formalism Based Reasoning Soft Computing (DE-588)4455833-8 gnd |
| subject_GND | (DE-588)4455833-8 |
| title | Soft computing and its applications |
| title_auth | Soft computing and its applications |
| title_exact_search | Soft computing and its applications |
| title_exact_search_txtP | Soft computing and its applications |
| title_full | Soft computing and its applications R.A. Aliev, R.R. Aliev |
| title_fullStr | Soft computing and its applications R.A. Aliev, R.R. Aliev |
| title_full_unstemmed | Soft computing and its applications R.A. Aliev, R.R. Aliev |
| title_short | Soft computing and its applications |
| title_sort | soft computing and its applications |
| topic | Soft Computing (DE-588)4455833-8 gnd |
| topic_facet | Soft Computing |
| work_keys_str_mv | AT alievrarafikazizogly softcomputinganditsapplications AT alievrr softcomputinganditsapplications |