Qualitative system identification with the use of on-line genetic algorithms

Abstract The major problem in building qualitative models via on-line qualitative system identification is how to filter the spurious constraints that are generated from the qualitative reasoning technique. This paper proposes a solution to this problem by an integration of genetic algorithms (GA) and qualitative reasoning. The paper will demonstrate the use of qualitative reasoning to partition the input quantity space into different subsystems, and implementation of GAs to filter and optimize the predicted constraints. The proposed method is verified by simulated examples that suggest the algorithm converges to the optimal point with high speed.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  Joseph Aguilar-Martin.  Qualitative control, diagnostic and supervision of complex processes , 1994 .

[3]  Sanjaya Addanki,et al.  Graphs of Models , 1991, Artif. Intell..

[4]  Benjamin J. Kaipers,et al.  Qualitative Simulation , 1989, Artif. Intell..

[5]  Benjamin Kuipers,et al.  Qualitative reasoning: Modeling and simulation with incomplete knowledge , 1994, Autom..

[6]  Pieter J. Mosterman,et al.  Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Luca Console,et al.  Readings in Model-Based Diagnosis , 1992 .

[8]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[9]  D. A. Linkens,et al.  Automatic modelling and analysis of dynamic physical systems using qualitative reasoning and bond graphs , 1993 .

[10]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artif. Intell..

[11]  Jorge Muniz Barreto,et al.  Building fuzzy-qualitative models for simulation and control , 1996, Simul. Pract. Theory.

[12]  Y. M. Enab.  Qualitative controller for linear systems , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[13]  Johan de Kleer,et al.  A Qualitative Physics Based on Confluences , 1984, Artif. Intell..

[14]  Benjamin Kuipers,et al.  Commonsense Reasoning about Causality: Deriving Behavior from Structure , 1984, Artif. Intell..

[15]  Leonard Bolc,et al.  Search methods for artificial intelligence , 1992 .

[16]  Jan Lunze,et al.  On qualitative identification of linear dynamical systems , 1994 .

[17]  Brian Falkenhainer,et al.  Compositional Modeling: Finding the Right Model for the Job , 1991, Artif. Intell..

[18]  A. B. Rad,et al.  Qualitative modeling and control of dynamic systems , 1997 .

[19]  M. Pasquier,et al.  Application Issues to Integrate Process Supervision , 1997 .

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .