Paper Info
Title
Model inference of a dynamic system by fuzzy learning of geometric structures
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.
Year
DOI
Venue
2006
10.1007/11881599_82
FSKD
Keywords
Field
DocType
connection model,model inference,fuzzy unmerging function,large varying action,reasonable time region,geometric structure,shorter time region,larger varying action,reasonable model region,time series data,fuzzy learning,dynamic system
Information system,Time series,Inference,Computer science,Fuzzy logic,Algorithm,Bellman equation,Knowledge extraction,Software maintenance,Dynamical system
Conference
Volume
ISSN
ISBN
4223
0302-9743
3-540-45916-2
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Kaijun Wang1704.86
Junying Zhang215321.12
Jingxuan Wei3628.42