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Model Inference of a Dynamic System by Fuzzy Learning of Geometric Structures

  • Kaijun Wang
  • Junying Zhang
  • Jingxuan Wei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

One of difficult tasks on dynamic systems is the exploration of connection models of variables from time series data. Reasonable time regions for constructing the models are crucial to avoid improper models or the loss of important information. We propose fuzzy learning of geometric structures to find reasonable time regions and proper models to reveal varying laws of system. By comparing values of fuzzy merging function for shorter time regions and fuzzy unmerging function for larger varying actions, reasonable model regions are inferred. Experimental results (for both simulated and real data) show that the proposed method is very effective in finding connection models adaptive to the evolution of a dynamic system, and it detected large varying actions in the regions below preset minimal region length, whereas the non-fuzzy learning method failed.

Keywords

Geometric Structure Time Series Data Model Region Time Region Model Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kaijun Wang
    • 1
  • Junying Zhang
    • 1
  • Jingxuan Wei
    • 2
  1. 1.School of computer science and engineeringXidian UniversityXi’anP.R. China
  2. 2.Dept of applied mathematicsXidian UniversityXi’anP.R. China

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