F.E.Cellier - Veröffentlichungen betreffend Diffuses Induktives Schliessen

Bücher

  1. Cellier, F.E. (1991), Continuous System Modeling, Springer-Verlag, New York.

Kapitel in Büchern

  1. Cellier, F.E. (1991), General System Problem Solving Paradigm for Qualitative Modeling, Qualitative Simulation Modeling and Analysis (P.A. Fishwick and P.A. Luker, eds.), Springer-Verlag, New York, pp.51-71.

  2. Cellier, F.E. (2000), Simulation und Vorhersage: Sind Simulationstechniker die Propheten der Neuzeit? Gedanken zur Zeit - Festschrift anlässlich des 60sten Geburtstags von Herrn Prof. Dr. Bernd Schmidt (R. Rimane, Ed.), SCS Publishing, Erlangen, Germany, pp. 1-28.

Zeitschriftenartikel

  1. Cellier, F.E. (1987), Prisoner's Dilemma Revisited - A New Strategy Based on the General System Problem Solving Framework, Intl. J. General Systems, 13(4), pp.323-332.

  2. Cellier, F.E. (1987), Qualitative Simulation of Technical Systems by Means of the General System Problem Solving Framework, Intl. J. General Systems, 13(4), pp.333-344.

  3. Cellier, F.E., and Á. de Albornoz (1998), The Problem of Distortions in Reconstruction Analysis, Systems Analysis, Modelling, Simulation, 33(1), pp.1-19.

  4. Cellier, F.E., and J. López (1995), Causal Inductive Reasoning: A New Paradigm for Data-Driven Qualitative Simulation of Continuous-Time Dynamical Systems, Systems Analysis Modelling Simulation, 18(1), pp.27-43.

  5. Cellier, F.E., J. López, À. Nebot, and G. Cembrano (2010), Confidence Measures for Predictions in Fuzzy Inductive Reasoning, Intl. J. General Systems, 39(8), pp.839-853.

  6. Cellier, F.E., and F. Mugica (1995), Inductive Reasoning Supports the Design of Fuzzy Controllers, J. Intelligent & Fuzzy Systems, 3(1), pp.71-85.

  7. Cellier, F.E., À. Nebot, F. Mugica and Á. de Albornoz (1996), Combined Qualitative/Quantitative Simulation Models of Continuous-Time Processes Using Fuzzy Inductive Reasoning Techniques, Intl. J. General Systems, 24(1-2), pp.95-116.

  8. Cellier, F.E., and Y.D. Pan (1995), Fuzzy Adaptive Recurrent Counterpropagation Neural Networks: A Tool for Efficient Implementation of Qualitative Models of Dynamic Processes, J. Systems Engineering, 5(4), pp.207-222.

  9. Cellier, F.E., and D.W. Yandell (1987), SAPS-II: A New Implementation of the Systems Approach Problem Solver, Intl. J. General Systems, 13(4), pp.307-322.

  10. de Albornoz, Á., and F.E. Cellier (1994), Building Intelligence into an Autopilot - Using Qualitative Simulation to Support Global Decision Making, Simulation, 62(6), pp.354-363.

  11. Escobet, A., À. Nebot, and F.E. Cellier (2007), Fault Detection and Identification Using FIRFMS, Intl. J. General Systems, 36(3), pp.347-374.

  12. Escobet, A., À. Nebot, and F.E. Cellier (2008), Visual-FIR: A Tool for Model Identification and Prediction of Dynamical Complex Systems, Simulation Modeling Practices and Theory, 16(1), pp.76-92.

  13. Escobet, A., À. Nebot, and F.E. Cellier (2011), Fault Diagnosis System Based on Fuzzy Logic: Application to a Valve Actuator Benchmark, J. Intelligent Fuzzy Systems, 22(4), pp.155-171.

  14. López, J., F.E. Cellier, and G. Cembrano (2011), Estimating the Horizon of Predictability in Time Series Predictions Using Fuzzy Modeling Tools, Intl. J. General Systems, 40(3), pp.263-282.

  15. Mirats, J.M., F.E. Cellier, and R.M. Huber (2002), Variable Selection Procedures and Efficient Suboptimal Mask Search Algorithms in Fuzzy Inductive Reasoning, Intl. J. General Systems, 31(5), pp.469-498.

  16. Mirats, J.M., F.E. Cellier, and R.M. Huber (2004), Reconstruction Analysis Based Algorithm to Decompose a Complex System into Subsystems, Intl. J. General Systems, 33(5), pp.527-551.

  17. Mirats, J.M., F.E. Cellier, R.M. Huber, and S.J. Qin (2002), On the Selection of Variables for Qualitative Modelling of Dynamical Systems, Intl. J. General Systems, 31(5), pp.435-467.

  18. Nebot, À., F.E. Cellier, R. Carvajal, and F. Mugica (2009), Fuzzy Inductive Reasoning for Variable Selection Analysis and Modelling of Biological Systems, Intl. J. General Systems, 38(8), pp.793-811.

  19. Nebot, À., F.E. Cellier, and D.A. Linkens (1996), Synthesis of an Anaesthetic Agent Administration System Using Fuzzy Inductive Reasoning, Artificial Intelligence in Medicine, 8(3), pp.147-166.

  20. Nebot, À., F.E. Cellier, and M. Vallverdú (1998), Mixed Quantitative/Qualitative Modeling and Simulation of the Cardiovascular System, Computer Methods and Programs in Biomedicine, 55(2), pp.127-155.

  21. Nebot, À., F. Mugica, F.E. Cellier, and M. Vallverdú (2003), Modeling and Simulation of the Central Nervous System Control with Generic Fuzzy Models, Simulation, 79(5), pp.648-669.

  22. Uhrmacher, A.M., F.E. Cellier, and R.J. Frye (1997), Applying Fuzzy-Based Inductive Reasoning to Analyze Qualitatively the Dynamic Behavior of an Ecological System, International Journal on Applied Artificial Intelligence in Natural Resource Management, 11(2), pp.1-10.

  23. Vesanterä, P.J., and F.E. Cellier (1989), Building Intelligence into an Autopilot Using Qualitative Simulation to Support Global Decision Making, Simulation, 52(3), pp.111-121.

Hauptvorträge bei Tagungen

  1. Cellier, F.E. (1996), Mixed Quantitative and Qualitative Modeling: Means for Dealing With System Uncertainty, Proc. 15th Benelux Meeting on Systems and Control, Mierlo, The Netherlands, pp.111-123.

  2. Cellier, F.E. (2000), Simulation und Vorhersage: Sind Simulationstechniker die Propheten der Neuzeit? Gedanken zur Zeit - Festschrift anlässlich des 60sten Geburtstags von Herrn Prof. Dr. Bernd Schmidt (R. Rimane, Ed.), SCS Publishing, Erlangen, Germany, pp. 1-28.

  3. Cellier, F.E. (2001), Die Vorhersage makroökonomischer Prozesse: Wissenschaft, Kunst oder Hochstapelei?, Proc. 15th ASIM Symposium, Paderborn, Germany, pp.1-10.

  4. Cellier, F.E., and J. López (1995), Causal Inductive Reasoning: A New Paradigm for Data-Driven Qualitative Simulation of Continuous-Time Dynamical Systems, Proc. Systems Analysis Modelling Simulation, Berlin, Germany, pp.27-43.

Andere Tagungsbeiträge

  1. Cellier, F.E. (1988), Qualitative Simulation of Biomedical Processes: An Aid in Decision Making, Proc. World Congress on Medical Physics and Biomedical Engineering, San Antonio, Texas, p. 228.

  2. Cellier, F.E. (1991), Qualitative Modeling and Simulation - Promise or Illusion?, Panel discussion with R. Doyle, Y. Iwasaki, and E. Scarl, Proc. Winter Simulation Conference, Phoenix, Arizona, pp. 1086-1090.

  3. Cellier, F.E. (1993), Mixed Quantitative and Qualitative Modeling and Simulation, Proc. ESS'93, European Simulation Symposium, Delft, The Netherlands, pp. 761-762.

  4. Cellier, F.E., and Á. de Albornoz (1997), The Problem of Distortions in Reconstruction Analysis, Proc. IIGSS'97, 2nd Workshop of the International Institute for General Systems Studies, San Marcos, Texas.

  5. Cellier, F.E., J. López, À. Nebot, and G. Cembrano (1996), Means for Estimating the Forecasting Error in Fuzzy Inductive Reasoning, Proc. ESM'96, European Simulation MultiConference, Budapest, Hungary, pp. 654-660.

  6. Cellier, F.E., and F. Mugica (1992), Systematic Design of Fuzzy Controllers Using Inductive Reasoning, Proc. IEEE Intelligent Control Conference, Glasgow, Scotland, pp. 198-203.

  7. Cellier, F.E. and À. Nebot (2004), Multi-resolution Time-Series Prediction Using Fuzzy Inductive Reasoning, Proc. IJCNN'04, IEEE Intl. Joint Conf. on Neural Networks, Budapest, Hungary, vol. 2, pp. 1621-1624.

  8. Cellier, F.E., À. Nebot, F. Mugica, and Á. de Albornoz (1992), Combined Qualitative/Quantitative Simulation Models of Continuous-Time Processes Using Fuzzy Inductive Reasoning Techniques, Proc. SICICA'92, IFAC Symposium on Intelligent Components and Instruments for Control Applications, Málaga, Spain, pp. 589-593.

  9. Cellier, F.E., and N. Roddier (1991), Qualitative State Spaces: A Formalization of the Naïve Physics Approach to Knowledge-based Reasoning, Proc. AI, Simulation and Planning in High Autonomy Systems, Cocoa Beach, Florida, pp. 40-49.

  10. Cellier, F.E. and V. Sanz (2009), Mixed Quantitative and Qualitative Simulation in Modelica, Proc. 7th International Modelica Conference, Como, Italy, pp. 86-95.

  11. de Albornoz, Á., and F.E. Cellier (1993), Qualitative Simulation Applied to Reason Inductively About the Behavior of a Quantitatively Simulated Aircraft Model, Proc. QUARDET'93, IMACS Intl. Workshop on Qualitative Reasoning and Decision Technologies, Barcelona, Spain, pp. 711-721.

  12. de Albornoz, Á., and F.E. Cellier (1993), Variable Selection and Sensor Fusion in Automatic Hierarchical Fault Monitoring of Large Scale Systems, Proc. QUARDET'93, IMACS Intl. Workshop on Qualitative Reasoning and Decision Technologies, Barcelona, Spain, pp. 722-734.

  13. de Albornoz, Á., J. Sardá, and F.E. Cellier (1994), Structure Identification in Variable Structure Systems by Means of Qualitative Simulation, Proc. ESM'94, European Simulation MultiConference, Barcelona, Spain, pp. 486-491.

  14. Escobet, A., R.M. Huber, À. Nebot, and F.E. Cellier (2000), Enhanced Equal Frequency Partition Method for the Identification of a Water Demand System, Proc. AI, Simulation and Planning in High Autonomy Systems, Tucson, Arizona, pp. 209-215.

  15. Escobet, A., À. Nebot, and F.E. Cellier (1999), Model Acceptability Measure for the Identification of Failures in Qualitative Fault Monitoring Systems, Proc. ESM'99, European Simulation MultiConference, Warsaw, Poland, pp. 339-347.

  16. Escobet, A., À. Nebot, and F.E. Cellier (2004), Visual-FIR: A New Platform for Modeling and Prediction of Dynamical Systems, Proc. SCSC'04, Summer Computer Simulation Conference, San Jose, California, pp. 229-234.

  17. Escobet, A., À. Nebot, and F.E. Cellier (2007), VisualBlock-FIR for Fault Detection and Identification: Application to the DAMADICS Benchmark Problem, Proc. MICAI'07, 6th Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, Springer-Verlag, Lecture Notes on Artificial Intelligence, LNAI 4827, pp. 1173-1183.

  18. Li, D., and F.E. Cellier (1990), Fuzzy Measures in Inductive Reasoning, Proc. Winter Simulation Conference, New Orleans, LA, pp. 527-538.

  19. López, J., and F.E. Cellier (1999), Improving the Forecasting Capability of Fuzzy Inductive Reasoning by Means of Dynamic Mask Allocation, Proc. ESM'99, European Simulation MultiConference, Warsaw, Poland, pp. 355-362.

  20. López, J., G. Cembrano, and F.E. Cellier (1996), Time Series Prediction Using Fuzzy Inductive Reasoning: A Case Study, Proc. ESM'96, European Simulation MultiConference, Budapest, Hungary, pp. 765-770.

  21. Moorthy. M., F.E. Cellier, and J.T. LaFrance (1998), Predicting U.S. Food Demand in the 20th Century: A New Look at System Dynamics, Proc. SPIE Conference 3369: "Enabling Technology for Simulation Science II", part of AeroSense'98, Orlando, Florida, pp. 343-354.

  22. Mugica, F., and F.E. Cellier (1993), A New Fuzzy Inferencing Method for Inductive Reasoning, Proc. Intl. Symp. Artificial Intelligence, Monterrey, Mexico, pp. 372-379.

  23. Mugica, F., and F.E. Cellier (1994), Automated Synthesis of a Fuzzy Controller for a Cargo Ship Steering by Means of Qualitative Simulation, Proc. ESM'94, European Simulation MultiConference, Barcelona, Spain, pp. 523-528.

  24. Nebot, À., F.E. Cellier, and D.A. Linkens (1993), Controlling an Anaesthetic Agent by Means of Fuzzy Inductive Reasoning, Proc. QUARDET'93, IMACS Intl. Workshop on Qualitative Reasoning and Decision Technologies, Barcelona, Spain, pp. 345-356.

  25. Nebot, À., and F.E. Cellier (1994), Preconditioning of Measurement Data for the Elimination of Patient-Specific Behavior in Qualitative Modeling of Medical Systems, Proc. CISS'94, First Joint Conf. of Intl. Simulation Societies, Zurich, Switzerland, pp. 584-588.

  26. Nebot À., and F.E. Cellier (1994), Dealing With Incomplete Data Records in Qualitative Modeling and Simulation of Biomedical Systems, Proc. CISS'94, First Joint Conf. of Intl. Simulation Societies, Zurich, Switzerland, pp. 605-610.

  27. Nebot, À., S. Medina, and F.E. Cellier (1994), The Causality Horizon: Limitations to Predictability of Behavior Using Fuzzy Inductive Reasoning, Proc. ESM'94, European Simulation MultiConference, Barcelona, Spain, pp. 492-496.

  28. Sarjoughian, H.S., B.P. Zeigler, and F.E. Cellier (1998), Evaluating Model Abstractions: A Quantitative Approach, Proc. SPIE Conference 3369: "Enabling Technology for Simulation Science II", part of AeroSense'98, Orlando, Florida, pp. 59-70.

Dissertationen

  1. de Albornoz, Á. (1996), Inductive Reasoning and Reconstruction Analysis: Two Complementary Tools for Qualitative Fault Monitoring of Large-Scale Systems, Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain.

  2. Escobet, Á. (2012), Generació de Decisions davant d'Incerteses, Departament de Disseny i Programació de Sistemes, Universitat Politècnica de Catalunya, Manresa, Spain.

  3. López, J. (1999), Time Series Prediction Using Inductive Reasoning Techniques, Organització i Control de Sistemes Industrials, Universitat Politècnica de Catalunya, Barcelona, Spain.

  4. Mirats, J.M. (2001), Qualitative Modeling of Complex Systems by Means of Fuzzy Inductive Reasoning: Variable Selection and Search Space Reduction, Tecnologies Avançades de la Producció, Universitat Politècnica de Catalunya, Barcelona, Spain.

  5. Mugica, F. (1995), Diseño Sistemático de Controladores Difusos Usando Razonamiento Inductivo, Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain.

  6. Nebot, À. (1994), Qualitative Modeling and Simulation of Biomedical Systems Using Fuzzy Inductive Reasoning, Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain.

  7. Pan, Y.D. (1994), Fuzzy Adaptive Recurrent Counterpropagation Neural Networks: A Tool for Efficient Implementation of Qualitative Models of Dynamic Processes, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

  8. Sarjoughian, H.S. (1995), Inductive Modeling of Discrete-Event Systems: A TMS-Based Non-Monotonic Reasoning Approach, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

  9. Soto, M. (2010), Building an Artificial Cerebellum Using a System of Distributed Q-Learning Agents, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

MS Arbeiten

  1. Moorthy, M. (1999), Mixed Structural and Behavioral Models for Predicting the Future Behavior of Some Aspects of the Macroeconomy, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

  2. Soto, M. (2004), On-line Q-learner Using Moving Prototypes, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

  3. Vesanterä, P.J. (1988), Qualitative Flight Simulation: A Tool for Global Decision Making, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

Interne Berichte

  1. Cellier, F.E., and D.W. Yandell (1986), SAPS-II: Raw Data Analysis in CTRL-C, Dept. of Electr. & Comp. Engr., University of Arizona, Tucson, AZ.

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Modifiziert: 16. Oktober 2013 -- © François Cellier