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Artificial intelligence in real time control 1994 (AIRTC'94) : a postprint volume from the IFAC symposium, Valencia, Spain, 3-5 October 1994 /
Artificial Intelligence is one of the new technologies that has contributed to the successful development and implementation of powerful and friendly control systems. These systems are more attractive to end-users shortening the gap between control theory applications. The IFAC Symposia on Artificia...
| Clasificación: | Libro Electrónico |
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| Autores Corporativos: | , |
| Otros Autores: | |
| Formato: | Electrónico Congresos, conferencias eBook |
| Idioma: | Inglés |
| Publicado: |
Oxford, UK :
Published for the International Federation of Automatic Control by Pergamon,
1995.
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| Edición: | First edition. |
| Colección: | IFAC Postprint Volume.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Artificial Intelligence in Real Time Control 1994 (AIRTC'94); Copyrigh Page; IFAC SYMPOSIUM ON ARTIFICIAL INTELLIGENCE IN REAL TIME CONTROL 1994; FOREWORD; Table of Contents; PART 1:PLENARY PAPERS; Chapter 1. Integrating Real-Time AI Techniques in Adaptive Intelligent Agents; 1. INTRODUCTION; 2. REAL TIME TECHNIQUES IN THE AGENT ARCHITECTURE; 3. REAL-TIME AI TECHNIQUES IN THE AGENT'S REASONING METHODS; 4. REAL-TIME AI TECHNIQUES IN THE AGENT'S CONTROL STRATEGY; 5. EMERGENT REAL-TIME PROPERTIES IN AN AGENT'S BEHAVIOR; 6. CONCLUSIONS; 7. REFERENCES
- CHAPTER 2. NEURAL NETWORK BASED ADAPTIVE CONTROL1. INTRODUCTION; 2. NEURAL NETWORK BASED CONTROL; 3. LEARNING AND ADAPTATION; 4. NN BASED ADAPTIVE CONTROLLERS CLASSIFICATION; 5. NN ADAPTIVE CONTROL WITH FAST ADAPTATION SPEED; 6. STABILITY ANALYSIS; 7. IMPLEMENTATIONS; 8. CONCLUSIONS; ACKNOWLEDGEMENT; 9. REFERENCES; CHAPTER 3. A COMPUTATIONAL INTELLIGENCE PERSPECTIVE ON PROCESS MONITORING AND OPTIMIZATION; 1. INTRODUCTION; 2. THE FUNCTIONAL-LINK NET: EFFICIENCY IN LEARNING A MODEL OF A PROCESS; 3. OPTIMIZATION; 4 ASSOCIATIVE MEMORIZATION AND RECALL; 5 THE COMPUTATIONAL INTELLIGENCE PERSPECTIVE
- 6. EXAMPLES OF PROCESS MONITORING AND OPTIMIZATION TASKS7. REFERENCES; CHAPTER 4. TRENDS IN ARTIFICIAL INTELLIGENCE APPLICATIONS FOR REAL-TIME CONTROL (A SPECULATIVE STUDY); 1. INTRODUCTION; 2. REAL-TIME SYSTEMS; 3. AI METHODS AND TIME CONSTRAINTS; 4. AI APPLICATIONS IN REAL-TIME; 5. TRENDS IN REAL-TIME AI; 6. CONCLUSIONS; ACKNOWLEDGEMENT; REFERENCES; PART 2:FUZZY LOGIC AND NEURAL NETWORKS; CHAPTER 5. DYNAMIC ANALYSIS OF WEIGTHED-OUTPUT FUZZY CONTROL SYSTEMS; 1. INTRODUCTION; 2. STABILITY ANALYSIS; 3. WEIGTHED OUTPUT FUZZY CONTROLLERS; 4. CONCLUSIONS; 5. ACKNOWLEDGEMENTS; 6. REFERENCES
- CHAPTER 6. IDENTIFICATION OF FUZZY RULES FROM LEARNING DATA1. INTRODUCTION; 2. THE SIMPLIFIED COS-FUZZY CONTROLLER; 3. THE IDENTIFICATION ALGORITHM; 4. EXAMPLES; 5. CONCLUSIONS; REFERENCES; CHAPTER 7. ROBUST DESIGN OF FUZZY CONTROLLERS BASED ON SMALL GAIN CONDITIONS; 1. INTRODUCTION; 2. STABILITY ANALYSIS; 3. ROBUST DESIGN; 4. EXAMPLE; 5. CONCLUSIONS; 6. ACKNOWLEDGEMENT; 7. REFERENCES; CHAPTER 8. A FUZZY CONTROLLER FOR ACTIVATED SLUDGE WASTE WATER PLANTS; 1. INTRODUCTION; 2. AN EXAMPLE PLANT; 3. CONVENTIONAL CONTROL; 4. THE FUZZY CONTROLLER PROPOSED; 5. SIMULATION EXPERIMENTS AND RESULTS
- 6. CONCLUDING REMARKSAcknowledgements; 7. REFERENCES; CHAPTER 9. AN ADVANCED FUZZY CONTROLLER FOR TRAFFIC LIGHTS; 1. INTRODUCTION; 2. FUZZY LOGIC AND TRAFFIC LIGHT DESIGN; 3. THE PROBLEM CONSIDERED; 4. SIMULATION EXPERIMENTS AND THEIR RESULTS; 5. CONCLUDING REMARKS; Acknowledgements; 6. REFERENCES; CHAPTER 10.REAL TIME FUZZY CONTROL OF COLUMN FLOTATION PROCESS; 1. INTRODUCTION; 2. FUZZY CONTROLLER DESCRIPTION; 3. GRAPHICAL USER INTERFACE; 4. FUZZY CONTROLLER APPLICATION AND RESULTS; 5. CONCLUSIONS; 6. REFERENCES; CHAPTER 11. ANALYSIS OF RULEBASE COHERENCE IN FUZZY CONTROL SYSTEMS