| 摘要 | 第1-8页 |
| Abstract | 第8-13页 |
| Chapter 1 Introduction | 第13-25页 |
| ·Research motivation | 第15-17页 |
| ·Fuzzy Logic | 第17-18页 |
| ·Artificial Neural Network | 第18-19页 |
| ·Integration of Fuzzy Inference System and Artificial Neural Network | 第19-20页 |
| ·Research Outline | 第20-22页 |
| ·Organization of the Dissertation | 第22-25页 |
| Chapter 2 Artificial Neural Network and Fuzzy Set Theory | 第25-48页 |
| ·Introduction to ANN | 第25-28页 |
| ·Basic Artificial Neural Network Units | 第28-33页 |
| ·Model of a neuron | 第28-31页 |
| ·Network Architecture | 第31-33页 |
| ·Neural Network Learning | 第33-36页 |
| ·Supervised and Unsupervised Learning | 第34-35页 |
| ·Learning Rules | 第35-36页 |
| ·Introduction to Fuzzy Set Theory | 第36-38页 |
| ·Fuzzy Sets and Fuzzy Logic Function | 第38-41页 |
| ·A Fuzzy Logic Controller | 第41-46页 |
| ·Fuzzification | 第41-42页 |
| ·Fuzzy Logic Operations | 第42-44页 |
| ·Defuzzification Operator | 第44-46页 |
| ·Conclusion | 第46-48页 |
| Chapter 3 Dynamical System and Fuzzy Neural Networks | 第48-77页 |
| ·Dynamical System Modeling | 第48-49页 |
| ·Problem Definition | 第49-51页 |
| ·Fuzzy Neural Networks | 第51-59页 |
| ·Fuzzy Rule-based Neural Network Architecture | 第52-55页 |
| ·Two-Stage Training in Fuzzy Neural Networks | 第55-58页 |
| ·Fuzzy Computation in Prediction Stage | 第58页 |
| ·Fuzzification of the Inputs | 第58-59页 |
| ·The DTFN Fuzzy Neural Architecture | 第59-65页 |
| ·The DFNA Neuronal Model | 第61-62页 |
| ·The DFNA Architecture | 第62-63页 |
| ·Characteristics of the DFNA Network | 第63-65页 |
| ·Back propagation Neural Network Training | 第65-70页 |
| ·Two-layer Network | 第65-67页 |
| ·Three-layer Network | 第67页 |
| ·Training Algonthm | 第67-70页 |
| ·Back Propagation Neural Network | 第70页 |
| ·Neural Network Predictive Control | 第70-76页 |
| ·Model Predictive Control and System Identification | 第71-72页 |
| ·Predictive Control | 第72-73页 |
| ·Using Neural Network Predictive Control | 第73-76页 |
| ·Conclusion | 第76-77页 |
| Chapter 4 Dynamic Tracking Control System Based on Dual Fuzzy Neural Network | 第77-95页 |
| ·Introduction | 第77-78页 |
| ·Fuzzy Neural Networks Model Based on DTFN | 第78-81页 |
| ·Concept | 第78-79页 |
| ·Identification Scheme | 第79-81页 |
| ·Importance of Steady State Data | 第81页 |
| ·Description of the Dual Fuzzy Neural Network | 第81-88页 |
| ·System Adaptation with Single Fuzzy Neural Network | 第81-86页 |
| ·A Summary of the System Adaptation via Single Fuzzy Neural Networks | 第86页 |
| ·System Adaptation with Dual Fuzzy Neural Network | 第86-88页 |
| ·Discussion on Selection of Controller Type and Switching Issues | 第88-94页 |
| ·Artificial Neural Network based Switching and Tuning Supervisor | 第88-90页 |
| ·Fuzzy-logic-based Switching and Tuning Supervisor | 第90-94页 |
| ·Conclusions | 第94-95页 |
| Chapter 5 The DTFN Learning Algorithm as automation knowledge acquisition | 第95-109页 |
| ·Introduction | 第95页 |
| ·Knowledge Acquisition | 第95-99页 |
| ·Acquiring Knowledge from Domain Experts | 第95-97页 |
| ·Rule Induction from Data Acquisition Results | 第97-99页 |
| ·Knowledge Base Verification | 第99-101页 |
| ·Fuzzy Rule Base Consistency | 第99-100页 |
| ·Fuzzy Rule Base Completeness | 第100-101页 |
| ·The DTFN Learning Algorithm | 第101-108页 |
| ·Principle of the DTFN Learning Algorithm | 第102-106页 |
| ·Convergence of the DTFN Learning Algorithm | 第106-107页 |
| ·Characteristics of the DTFN Learning Algorithm | 第107-108页 |
| ·Conclusion | 第108-109页 |
| Chapter 6 DTFN Dynamic Tracking Control Applications | 第109-124页 |
| ·Introduction | 第109页 |
| ·DTFN Dynamic Tracking Control in Application | 第109-116页 |
| ·Description of the Magnetic Levitation Model | 第109-112页 |
| ·Experimental Apparatus | 第112-114页 |
| ·Simulation Environment | 第114-116页 |
| ·Simulink Implementation and Testing Result | 第116-124页 |
| Chapter 7 Conclusions and Further Works | 第124-126页 |
| ACKNOWLEDGMENTS | 第126-127页 |
| BIBLIOGRAPHY | 第127-133页 |
| PUBLICATIONS | 第133-134页 |
