scholarly journals IDENTIFICATION OF EXCITABLE MEDIA USING A SCALAR COUPLED MAP LATTICE MODEL

2010 ◽  
Vol 20 (07) ◽  
pp. 2137-2150 ◽  
Author(s):  
YUZHU GUO ◽  
YIFAN ZHAO ◽  
S. A. BILLINGS ◽  
DANIEL COCA ◽  
R. I. RISTIC ◽  
...  
Keyword(s):  
Least Squares ◽  
Lattice Model ◽  
Identification Problem ◽  
Coupled Map Lattice ◽  
Excitable Media ◽  
Local Dynamics ◽  
Orthogonal Least Squares ◽  
Least Squares Algorithm ◽  
Belousov Zhabotinsky Reaction

The identification problem for excitable media is investigated in this paper. A new scalar coupled map lattice (SCML) model is introduced and the orthogonal least squares algorithm is employed to determinate the structure of the SCML model and to estimate the associated parameters. A simulated pattern and a pattern observed directly from a real Belousov–Zhabotinsky reaction are identified. The identified SCML models are shown to possess almost the same local dynamics as the original systems and are able to provide good long term predictions.

Identification of Time-Varying Time Constants of Thermocouple Sensors and Its Application to Temperature Measurement

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Kenneth Kar ◽  
Akshya K. Swain ◽  
Robert Raine
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Time Constant ◽  
Identification Problem ◽  
Basis Functions ◽  
New Technique ◽  
Ratio Test ◽  
Time Varying ◽  
Time Constants ◽  
Orthogonal Least Squares

The present study addresses the problem of estimating time-varying time constants associated with thermocouple sensors by a set of basis functions. By expanding each time-varying time constant onto a finite set of basis sequences, the time-varying identification problem reduces to a parameter estimation problem of a time-invariant system. The proposed algorithm, to be called as orthogonal least-squares with basis function expansion algorithm, combines the orthogonal least-squares algorithm with an error reduction ratio test to include significant basis functions into the model, which results in a parsimonious model structure. The performance of the method was compared with a linear Kalman filter. Simulations on engine data have demonstrated that the proposed method performs satisfactorily and is better than the Kalman filter. The new technique has been applied in a Stirling cycle compressor. The sinusoidal variations in time constant are tracked properly using the new technique, but the linear Kalman filter fails to do so. Both model validation and thermodynamic laws confirm that the new technique gives unbiased estimates and that the assumed thermocouple model is adequate.

GOLS—Genetic orthogonal least squares algorithm for training RBF networks

2006 ◽  
Vol 69 (16-18) ◽  
pp. 2041-2064 ◽  
Author(s):  
A.M.S. Barreto ◽  
H.J.C. Barbosa ◽  
N.F.F. Ebecken
Keyword(s):  
Least Squares ◽  
Rbf Networks ◽  
Orthogonal Least Squares ◽  
Least Squares Algorithm
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