Artificial intelligence in real-time control 1992 : selected papers from the IFAC/IFIP/IMACS symposium, Delft, the Netherlands, 16-18 June 1992 /

The symposium had two main aims, to investigate the state-of-the-art in the application of artificial intelligence techniques in real-time control, and to bring together control system specialists, artificial intelligence specialists and end-users. Many professional engineers working in industry fee...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores Corporativos: International Federation of Automatic Control, International Federation for Information Processing, International Association for Mathematics and Computers in Simulation, IFAC Symposium on Artificial Intelligence in Real-Time Control
Otros Autores: Verbruggen, H. B. (Editor ), Rodd, M. G. (Editor )
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: Oxford : Published for the International Federation of Automatic Control by Pergamon Press, 1993.
Edición:First edition.
Colección:IFAC symposia series ; 1993, no. 6.
Temas:
Artificial intelligence > Congresses.
Real-time control > Congresses.
Intelligence artificielle > Congr�es.
Commande en temps r�eel > Congr�es.
TECHNOLOGY & ENGINEERING > Engineering (General)
Artificial intelligence.
Real-time control.
Congress
Conference papers and proceedings.
Actes de congr�es.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • LEARNING OPTIMAL OR NEAROPTIMAL CONTROL PATHSREFERENCES; PART I: THE METHODOLOGY OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN CONTROL SYSTEMS; Section II: Neural Net Control; CHAPTER 3. NEURAL NETWORKS APPLIED TO OPTIMAL FLIGHT CONTROL; 1 Introduction; 2 Neural Network as a Multipurpose Nonlinear Function; 3 Aircraft Model; 4 Optimal Control Problems; 5 Conclusions; 6 Acknowledgements; References; CHAPTER 4. ADAPTIVE NEURAL NETWORK CONTROL OF FES-INDUCED CYCLICAL LOWER LEG MOVEMENTS; INTRODUCTION; METHODS; CONCLUSION; REFERENCES
  • CHAPTER 5. REGULARIZATION AS A SUBSTITUTE FOR PRE-PROCESSING OF DATA IN NEURAL NETWORK TRAINING1 Introduction; 2 Neural Networks as adaptive models; 3 Regularization instead of pre-processing; 4 Example; 5 Conclusions; References; CHAPTER 6. NEURAL NETWORK MODELLING AND CONTROL OF A PLANT EXHIBITING THE JUMP PHENOMENA; INTRODUCTION; THE JUMP PHENOMENA PLANT; NEURAL NONLINEAR MODELLING; TRAINING ALGORITHMS; NEURAL MODELLING; NEURAL PREDICTION MODELLING; NEURAL INTERNAL MODEL CONTROL; FUTURE WORK; CONCLUSIONS; ACKNOWLEDGEMENT; REFERENCES
  • CHAPTER 7. NEURAL NETWORKS (METHODOLOGIES FOR PROCESS MODELLING AND CONTROL)ABSTRACT; INTRODUCTION; NEURAL NETWORK MODELLING; SIGMOIDAL FUNCTION NETWORKS; RADIAL BASIS FUNCTION NETWORKS; NEURAL NETWORK SOFTWARE SENSORS; DYNAMIC NEURAL NETWORKS; NEURAL NETWORK BASED CONTROL; CONCLUDING REMARKS; ACKNOWLEDGEMENTS; REFERENCES; CHAPTER 8. PARALLEL NONLINEAR DECOUPLING FOR PROCESS CONTROL
  • A NARMAX APPROACH; INTRODUCTION; NON-LINEAR MODEL REPRESENTATION; PARALLEL DECOUPLING; INVERSE-BASED NONLINEAR PROCESS CONTROL; SIMULATION EXAMPLE
  • A CHEMICAL REACTOR PROBLEM; CONCLUSIONS; ACKNOWLEDGEMENTS

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