Application of a priori information to assure identifiability of a mathematical model

1989 ◽  
Vol 56 (5) ◽  
pp. 582-585
Author(s):  
M. R. Romanovskii
Keyword(s):  
Mathematical Model ◽  
A Priori ◽  
A Priori Information ◽  
Priori Information

MODELING UNDER LOCAL STRUCTURE HYPOTHESES

1993 ◽  
Vol 03 (04) ◽  
pp. 445-451
Author(s):  
SERGE V. PLOTNIKOV
Keyword(s):  
Mathematical Model ◽  
Pattern Recognition ◽  
Local Structure ◽  
Characteristic Property ◽  
A Priori ◽  
A Priori Information ◽  
Typical Problem ◽  
Suggested Approach ◽  
Priori Information

A mathematical model of a typical problem of pattern recognition is described. Its characteristic property is that it pays special attention to a priori information concerning the local structure of the object under investigation. Algorithmic backgrounds (using convex analytic tools) of the suggested approach are presented.

Resolution Enhancement of Composite Spectra with Fractal Noise in Derivative Spectrometry

2000 ◽  
Vol 54 (5) ◽  
pp. 721-730 ◽  
Author(s):  
S. S. Kharintsev ◽  
D. I. Kamalova ◽  
M. Kh. Salakhov
Keyword(s):  
A Priori ◽  
Resolution Enhancement ◽  
Numerical Differentiation ◽  
A Priori Information ◽  
Experimental Ir ◽  
Composite Spectra ◽  
Self Similar ◽  
Priori Information ◽  
Fractal Noise ◽  
Derivative Spectrometry

The problem of improving the resolution of composite spectra with statistically self-similar (fractal) noise is considered within the framework of derivative spectrometry. An algorithm of the numerical differentiation of an arbitrary (including fractional) order of spectra is produced by the statistical regularization method taking into account a priori information on statistical properties of the fractal noise. Fractal noise is analyzed in terms of the statistical Hurst method. The efficiency and expedience of this algorithm are exemplified by treating simulated and experimental IR spectra.

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