Authors:
Elvis Omar Jara Alegria
;
Hugo Tanzarella Teixeira
and
Celso Pascoli Bottura
Affiliation:
State University of Campinas - UNICAMP, Brazil
Keyword(s):
Time Series Identification, State-dependent Parameter, Data Reordering.
Related
Ontology
Subjects/Areas/Topics:
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Nonlinear Signals and Systems
;
Optimization Algorithms
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
System Modeling
;
Systems Modeling and Simulation
Abstract:
This paper shows a detailed study about the Young’s algorithm for parameter estimation on ARX-SDP models
and proposes some improvements. To reduce the high entropy of the unknown parameters, data reordering
according to a state ascendant ordering is used on that algorithm. After the Young’s temporal reordering
process, the old data do not necessarily continue so. We propose to reconsider the forgetting factor, internally
used in the exponential window past, as a fixed and small value. This proposal improves the estimation
results, especially in the low data density regions, and improves the algorithm velocity as experimentally
shown. Other interesting improvement of our proposal is characterized by the flexibility to the changes on the
state-parameter dependency. This is important in a future On-Line version. Interesting features of the SDP
estimation algorithm for the case of ARX-SDP models with unitary regressors and the case with correlated
state-parameter are also studied. Finall
y a example shows our results using the INCA toolbox we developed
for our proposal.
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